Target-enriched multiplexed parallel analysis for assessment of risk for genetic conditions

ABSTRACT

The invention provides methods for assessment of risk for genetic conditions using target-enriched multiplexed parallel analysis, for example fetal risk for genetic conditions in prenatal testing. The methods of the invention utilize Target Capture Sequences (TACS) to thereby enrich for target sequences of interest, followed by massive parallel sequencing and statistical analysis of the enriched population. The methods of the disclosure can be used to determine carrier status of inheritable genetic abnormalities associated with genetic conditions and from this information the fetal risk of inheriting the genetic condition can be determined. Kits for carrying out the methods of the invention are also provided.

FIELD OF THE INVENTION

The invention is in the field of biology, medicine and chemistry, more in particular in the field of molecular biology and more in particular in the field of molecular diagnostics.

BACKGROUND OF THE INVENTION

The discovery of free fetal DNA (ffDNA) in maternal circulation (Lo, Y. M. et al. (1997) Lancet 350:485-487) was a landmark towards the development of non-invasive prenatal testing for chromosomal abnormalities and has opened up new possibilities in the clinical setting. However, direct analysis of the limited amount of ffDNA in the presence of an excess of maternal DNA is a great challenge for Non-Invasive Prenatal Testing (NIPT) of chromosomal abnormalities. The implementation of next generation sequencing (NGS) technologies in the development of NIPT has revolutionized the field. In 2008, two independent groups demonstrated that NIPT of trisomy 21 could be achieved using next generation massively parallel shotgun sequencing (MPSS) (Chiu, R. W. et al. (2008) Proc. Natl. Acad. Sci. USA 105:20458-20463; Fan, H. C. et al. (2008) Proc. Natl. Acad. Sci. USA 105:16266-162710). The new era of NIPT for chromosomal abnormalities has opened new possibilities for the implementation of these technologies into clinical practice. Biotechnology companies that are partly or wholly dedicated to the development of NIPT tests have initiated large-scale clinical studies towards their implementation (Palomaki, G. E. et al. (2011) Genet. Med. 13:913-920; Ehrich, M. et al. (2011) Am. J. Obstet. Gynecol. 204:205e1-11; Chen, E. Z. et al. (2011) PLoS One 6:e21791; Sehnert, A. J. et al. (2011) Clin. Chem. 57:1042-1049; Palomaki, G. E. et al. (2012); Genet. Med. 14:296-305; Bianchi, D. W. et al. (2012) Obstet. Gynecol. 119:890-901; Zimmerman, B. et al. (2012) Prenat. Diag. 32:1233-1241; Nicolaides, K. H. et al. (2013) Prenat. Diagn. 33:575-579; Sparks, A. B. et al. (2012) Prenat. Diagn. 32:3-9).

Initial NIPT approaches used massively parallel shotgun sequencing (MPSS) NGS methodologies (see e.g., U.S. Pat. Nos. 7,888,017; 8,008,018; 8,195,415; 8,296,076; 8,682,594; US Patent Publication 20110201507; US Patent Publication 20120270739). Thus, these approaches are whole genome-based, in which the entire maternal sample containing both maternal DNA and free fetal DNA is subjected to amplification, sequencing and analysis.

More recently, targeted-based NGS approaches for NIPT, in which only specific sequences of interest are sequenced, have been developed. For example, a targeted NIPT approach using TArget Capture Sequences (TACS) for identifying fetal chromosomal abnormalities using a maternal blood sample has been described (PCT Publication WO 2016/189388; US Patent Publication 2016/0340733; Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855).

Such targeted approaches require significantly less sequencing than the MPSS approaches, since sequencing is only performed on specific loci on the target sequence of interest rather than across the whole genome. Additional methodologies for NGS-based approaches are still needed, in particular approaches that can target specific sequences of interest, thereby greatly reducing the amount of sequencing needed as compared to whole genome-based approaches, as well as increasing the read-depth of regions of interest, thus enabling detection of low signal to noise ratio regions. In particular, additional methodologies are still needed that allow for genetic aberrations present in diminutive amounts in a sample can be reliably detected. Furthermore, methods that allow for both assessment of fetal genetic aberrations as well assessment of fetal risk of inheriting a genetic condition(s) are still needed.

SUMMARY OF THE INVENTION

This invention provides improved methods for enriching targeted genomic regions of interest to be analyzed by multiplexed parallel sequencing, wherein the methods allow for assessment of fetal risk of inherited genetic conditions. The methods can further allow for assessment of fetal risk of a genetic abnormality at a chromosome(s) of interest. Thus, the methods of the invention provide the ability to simultaneously assess fetal risk of inherited genetic conditions and fetal risk of genetic abnormalities in a single sample containing fetal DNA. The methods utilize a sample comprising maternal and fetal DNA and fetal risk of inherited genetic conditions is assessed by determining maternal carrier status at loci of interest associated with different genetic conditions, as well as determining paternal carrier status for those diseases in which there is a positive maternal carrier status.

Accordingly, in one aspect the invention pertains to a method of determining fetal risk of inheriting a genetic condition, the method comprising:

(a) preparing a sequencing library from a sample comprising maternal and fetal DNA;

(b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of sequences that bind to genomic regions of interest including variant allele loci of interest associated with different genetic conditions;

(c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;

(d) amplifying and sequencing the enriched library;

(e) performing statistical analysis on the enriched library sequences to thereby determine maternal carrier status at the loci of interest associated with different genetic conditions, wherein for a sample with a positive maternal carrier status, the method further comprises:

(f) obtaining a paternal DNA sample and performing steps (a)-(e) on the paternal DNA sample to determine paternal carrier status for those diseases in which there is a positive maternal carrier status; and

(g) determining fetal risk of inheriting a genetic condition based on maternal carrier status and, when (f) is performed, paternal carrier status.

In one embodiment:

-   -   (i) each member sequence within the pool of TACS is between         100-500 base pairs in length, each member sequence having a 5′         end and a 3′ end;     -   (ii) each member sequence binds to the same genomic sequence of         interest at least 50 base pairs away, on both the 5′ end and the         3′ end, from regions harboring Copy Number Variations (CNVs),         Segmental duplications or repetitive DNA elements; and     -   (iii) the GC content of the pool of TACS is between 19% and 80%,         as determined by calculating the GC content of each member         within the pool of TACS.

In one embodiment, the sample is a maternal plasma sample.

In one embodiment, the pool of TACS comprises members that bind to chromosomes 1-22, X and Y of the human genome. In one embodiment, each member sequence within the pool of TACS is at least 160 base pairs in length. In certain embodiments, the GC content of the pool of TACS is between 19% and 80% or is between 19% and 46%. Alternative % ranges for the GC content of the pool of TACS are described herein.

In one embodiment, the pool of TACS comprises a plurality of TACS families, wherein each member of a TACS family binds to the same target sequence of interest but with different start/stop positions on the sequence with respect to a reference coordinate system (i.e., binding of TACS family members to the target sequence is staggered) to thereby enrich for target sequences of interest, followed by massive parallel sequencing and statistical analysis of the enriched population. The use of families of TACS with the TACS pool that bind to each target sequence of interest, as compared to use of a single TACS within the TACS pool that binds to each target sequence of interest, significantly increases enrichment for the target sequences of interest, as evidenced by a greater than 50% average increase in read-depth for the family of TACS versus a single TACS.

Accordingly, in one embodiment, the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest.

In certain embodiments, each TACS family comprises at least 3 member sequences or at least 5 member sequences. Alternative numbers of member sequences in each TACS family are described herein. In one embodiment, the pool of TACS comprises at least 50 different TACS families. Alternative numbers of different TACS families within the pool of TACS are described herein. In certain embodiments, the start and/or stop positions for the member sequences within a TACS family, with respect to a reference coordinate system for the genomic sequence of interest, are staggered by at least 3 base pairs or by at least 10 base pairs. Alternative lengths (sizes) for the number of base pairs within the stagger are described herein.

In one embodiment, the variant allele loci of interest are associated with genetic conditions selected from the group of Achondroplasia, Alpha-1 Antitrypsin Deficiency, Antiphospholipid Syndrome, Autism, Autosomal Dominant Polycystic Kidney Disease, Autosomal Recessive Polycystic Kidney Disease, Inheritable Breast Cancer Gene, Charcot-Marie-Tooth, Inheritable Colon Cancer Gene, Crohn's Disease, Cystic Fibrosis, Dercum Disease, Duane Syndrome, Duchenne Muscular Dystrophy, Factor V Leiden Thrombophilia, Familial Hypercholesterolemia, Familial Mediterranean Fever, Fragile X Syndrome, Gaucher Disease, Hemochromatosis, Hemophilia, Holoprosencephaly, Huntington's Disease, Marfan Syndrome, Myotonic Dystrophy, Neurofibromatosis, Noonan Syndrome, Osteogenesis Imperfecta, Phenylketonuria, Poland Anomaly, Porphyria, Prostate Cancer, Retinitis Pigmentosa, Severe Combined Immunodeficiency (SCID), Sickle Cell Disease, Spinal Muscular Atrophy, Tay-Sachs, Thalassemia, WAGR Syndrome, Wilson Disease, and combinations thereof.

In another embodiment, the variant allele loci of interest are associated with genetic conditions selected from the group of Abetalipoproteinemia; Arthrogryposis Mental Retardation Seizures; Autosomal recessive polycystic kidney disease; Bardet Biedl syndrome 12; Beta thalassemia; Canavan disease; Choreacanthocytosis; Crigler Najjar syndrome, Type I; Cystic fibrosis; Factor V Leiden thrombophilia; Factor XI deficiency; Familial dysautonomia; Familial Mediterranean fever; Fanconi anemia (FANCG-related); Glycine encephalopathy (GLDC-related); Glycogen storage disease, Type 3; Glycogen storage disease, Type 7; GRACILE Syndrome; Inclusion body myopathy, Type 2; Isovaleric acidemia; Joubert syndrome, Type 2; Junctional epidermolysis bullosa, Herlitz type; Leber congenital amaurosis (LCAS-related); Leydig cell hypoplasia [Luteinizing Hormone Resistance]; Limb girdle muscular dystrophy, Type 2E; Lipoamide Dehydrogenase Deficiency [Maple syrup urine disease, Type 3]; Lipoprotein lipase deficiency; Long chain 3-hydroxyacyl-CoA dehydrogenase deficiency; Maple syrup urine disease, Type 1B; Methylmalonic acidemia (MMAA-related); Multiple sulfatase deficiency; Navajo neurohepatopathy [MPV17-related hepatocerebral mitochondrial DNA depletion syndrome]; Neuronal ceroid lipofuscinosis (MFSD8-related); Nijmegen breakage syndrome; Ornithine translocase deficiency [Hyperornithinemia-Hyperammonemia-Homocitrullinuria (HHH) Syndrome]; Peroxisome biogenesis disorders Zellweger syndrome spectrum (PEX1-related); Peroxisome biogenesis disorders Zellweger syndrome spectrum (PEX2-related); Phenylketonurea; Pontocerebellar hypoplasia, Type 2E; Pycnodysostosis; Pyruvate dehydrogenase deficiency (PDHB-related); Retinal Dystrophy (RLBP1-related) [Bothnia retinal dystrophy]; Retinitis pigmentosa (DHDDS-related); Sanfilippo syndrome, Type D [Mucopolysaccharidosis IIID]; Sickle-cell disease; Sjögren-Larsson syndrome; Tay-Sachs disease; Usher syndrome, Type 1F; 3 Methylcrotonyl CoA Carboxylase Deficiency 1; 3 Methylcrotonyl CoA Carboxylase Deficiency 2, and combinations thereof.

In one embodiment, the pool of TACS further comprises sequences that bind to chromosomes of interest for detecting fetal genetic abnormalities and step (e) further comprises performing statistical analysis on the enriched library sequences to thereby determine fetal risk of a genetic abnormality at the chromosome of interest. In one embodiment, the genetic abnormality is a chromosomal aneuploidy. In one embodiment, the chromosomes of interest include chromosomes 13, 18, 21, X and Y. In one embodiment, the genetic abnormality is a structural abnormality, including but not limited to copy number changes including microdeletions and microduplications, insertions, deletions, translocations, inversions and small-size mutations including point mutations.

In one embodiment, the pool of TACS is fixed to a solid support. For example, in one embodiment, the TACS are biotinylated and are bound to streptavidin-coated magnetic beads.

In one embodiment, amplification of the enriched library is performed in the presence of blocking sequences that inhibit amplification of wild-type sequences.

In one embodiment, members of the sequencing library that bind to the pool of TACS are partially complementary to the TACS.

In one embodiment, the statistical analysis comprises a score-based classification system. In on embodiment, sequencing of the enriched library provides a read-depth for the genomic sequences of interest and read-depths for reference loci and the statistical analysis comprises applying an algorithm that tests sequentially the read-depth of the loci of from the genomic sequences of interest against the read-depth of the reference loci, the algorithm comprising steps for: (a) removal of inadequately sequenced loci; (b) GC-content bias alleviation; and (c) ploidy status determination. In one embodiment, GC-content bias is alleviated by grouping together loci of matching GC content. In one embodiment, sequencing of the enriched library provides the number and size of sequenced fragments for TACS-specific coordinates and the statistical analysis comprises applying an algorithm that tests sequentially the fragment-size proportion for the genomic sequence of interest against the fragment-size proportion of the reference loci, the algorithm comprising steps for: (a) removal of fragment-size outliers; (b) fragment-size proportion calculation; and (c) ploidy status determination.

In another aspect, the invention pertains to a method of determining fetal risk of inheriting a genetic condition, the method comprising:

(a) preparing a sequencing library from a sample comprising maternal and fetal DNA;

(b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest, and further wherein:

-   -   (i) each member sequence within each TACS family is between         100-500 base pairs in length, each member sequence having a 5′         end and a 3′ end;     -   (ii) each member sequence binds to the same genomic sequence of         interest at least 50 base pairs away, on both the 5′ end and the         3′ end, from regions harboring Copy Number Variations (CNVs),         Segmental duplications or repetitive DNA elements; and     -   (iii) the GC content of the pool of TACS is between 19% and 80%,         as determined by calculating the GC content of each member         within each family of TACS;

(c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;

(d) amplifying and sequencing the enriched library; and

(e) performing statistical analysis on the enriched library sequences to thereby determine maternal carrier status at the loci of interest associated with different genetic conditions, wherein for a sample with a positive maternal carrier status, the method further comprises:

(f) obtaining a paternal DNA sample and performing steps (a)-(e) on the paternal DNA sample to determine paternal carrier status for those diseases in which there is a positive maternal carrier status; and

(g) determining fetal risk of inheriting a genetic condition based on maternal carrier status and, when (f) is performed, paternal carrier status.

In yet another aspect, the invention pertains to a method of determining the status of a genetic condition, the method comprising:

(a) preparing a sequencing library from a sample of DNA;

(b) hybridizing the sequencing library to a pool of TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of sequences that bind to genomic sequences of interest including variant allele loci of interest associated with different genetic conditions, wherein:

-   -   (i) each member sequence within the pool of TACS is between         100-500 base pairs in length, each member sequence having a 5′         end and a 3′ end;     -   (ii) each member sequence binds to the same genomic sequence of         interest at least 50 base pairs away, on both the 5′ end and the         3′ end, from regions harboring Copy Number Variations (CNVs),         Segmental duplications or repetitive DNA elements; and     -   (iii) the GC content of the pool of TACS is between 19% and 80%,         as determined by calculating the GC content of each member         within the pool of TACS.

(c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;

(d) amplifying and sequencing the enriched library;

(e) performing statistical analysis on the enriched library sequences to thereby determine the carrier status at the loci of interest associated with different genetic conditions.

In another aspect, kits for performing the methods of the invention are also encompassed.

BRIEF DESCRIPTION OF THE FIGURES

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

FIG. 1 is a schematic diagram of multiplexed parallel analysis of targeted genomic regions for non-invasive prenatal testing using TArget Capture Sequences (TACS).

FIG. 2 is a listing of exemplary chromosomal regions for amplifying TACS that bind to for example chromosomes 13, 18, 21 or X. A more extensive list is shown in Table 1 below.

FIG. 3 is a schematic diagram of TACS-based enrichment of a sequence of interest (bold line) using a single TACS (left) versus TACS-based enrichment using a family of TACS (right).

FIGS. 4A-4B are graphs showing enrichment using families of TACS versus a single TACS, as illustrated by increase in the average read-depth. FIG. 4A shows loci enriched using a family of TACS (red dots) as compared to loci enriched using a single TACS (blue dots), with different target sequences shown on the x-axis and the fold change in read-depth shown on the y-axis. FIG. 4B is a bar graph illustrating the average fold-increase in read-depth (54.7%) using a family of TACS (right) versus a single TACS (left).

FIG. 5 is a plot graph illustrating minor allele frequencies (MAFs) of various loci associated with the indicated genetic conditions, as computed from a mixed sample containing maternal and fetal DNA. The x-axis is an index of samples. The y-axis shows the % MAF. The MAF value is dependent on the maternal fraction present in the mixed sample. MAF values above a certain threshold (not shown here) imply the presence of a genetic condition in the maternal sample (i.e., the maternal sample is assigned as a maternal carrier). cfDNA obtained from 1199 pregnancy samples and DNA from the biological father for each sample were subjected to in-solution targeted enrichment, using TACS, followed by NGS. The results show a total of 325 mutations that were identified in 266 pregnant samples, 78 of which were unique pathogenic mutations.

In the same dataset trisomy 21 (T21) was detected in 11 samples, trisomy 18 (T18) was detected in two samples, trisomy 13 (T13) was detected in one sample, sex chromosomal abnormalities (SCA) were detected in two samples and microdeletions (MD) in 4 samples. All aneuploidies, SCA and MD (20/20) were confirmed by karyotyping or arrayCGH.

FIG. 6 is a dot plot graph showing results of a fragments-based test for detecting increased numbers of smaller-size fragments in a mixed sample. An abnormal, aneuploid sample, with an estimated fetal fraction of 2.8%, was correctly detected using this method. The black dots are individual samples. The x-axis shows the sample index. The y-axis shows the score result of the fragments-size based method. A score result greater than the threshold shown by the grey line indicates a deviation from the expected size of fragments illustrating the presence of aneuploidy.

FIGS. 7 to 12 disclose graphs from the experiments as shown herein.

Table 1 below shows preferred and exemplary TACS positions.

Chr. Start Stop GC content Condition chr1 26764610 26764830 0.514 Retinitis pigmentosa DHDDS related chr1 100316484 100316734 0.340 Glycogen storage disease type 3 chr1 100316495 100316745 0.348 Glycogen storage disease type 3 chr1 100340820 100341070 0.344 Glycogen storage disease type 3 chr1 100341945 100342195 0.340 Glycogen storage disease type 3 chr1 100341965 100342215 0.352 Glycogen storage disease type 3 chr1 100346760 100347010 0.380 Glycogen storage disease type 3 chr1 100350043 100350293 0.336 Glycogen storage disease type 3 chr1 100350098 100350298 0.370 Glycogen storage disease type 3 chr1 100366153 100366383 0.413 Glycogen storage disease type 3 chr1 100368202 100368462 0.369 Glycogen storage disease type 3 chr1 100378968 100379228 0.304 Glycogen storage disease type 3 chr1 100379008 100379188 0.317 Glycogen storage disease type 3 chr1 100379013 100379213 0.340 Glycogen storage disease type 3 chr1 100381830 100382080 0.280 Glycogen storage disease type 3 chr1 100381939 100382189 0.292 Glycogen storage disease type 3 chr1 100382143 100382393 0.356 Glycogen storage disease type 3 chr1 100476839 100477099 0.342 Arthrogryposis Mental Retardation Seizures chr1 100476868 100477058 0.337 Arthrogryposis Mental Retardation Seizures chr1 150769145 150769405 0.462 Pycnodysostosis chr1 150769182 150769352 0.488 Pycnodysostosis chr1 169518919 169519179 0.423 Factor V Leiden thrombophilia chr1 183184472 183184732 0.508 Junctional epidermolysis bullosa Herlitz type chr1 183184492 183184752 0.519 Junctional epidermolysis bullosa Herlitz type chr2 26417929 26418179 0.504 Long chain 3 hydroxyacyl CoA dehydrogenase deficiency chr2 26426889 26427149 0.462 Long chain 3 hydroxyacyl CoA dehydrogenase deficiency chr2 26426904 26427134 0.487 Long chain 3 hydroxyacyl CoA dehydrogenase deficiency chr2 27535794 27536004 0.567 Navajo neurohepatopathy MPV17 related hepatocerebral mitochondrial DNA depletion syndrome chr2 48914965 48915215 0.380 Leydig cell hypoplasia Luteinizing Hormone Resistance chr2 48915152 48915402 0.388 Leydig cell hypoplasia Luteinizing Hormone Resistance chr2 48915307 48915557 0.428 Leydig cell hypoplasia Luteinizing Hormone Resistance chr2 48915780 48916040 0.412 Leydig cell hypoplasia Luteinizing Hormone Resistance chr2 48950704 48950954 0.372 Leydig cell hypoplasia Luteinizing Hormone Resistance chr2 219525688 219525938 0.576 GRACILE syndrome chr2 219525751 219526001 0.580 GRACILE syndrome chr2 219525833 219526083 0.532 GRACILE syndrome chr2 219525881 219526131 0.504 GRACILE syndrome chr2 219526345 219526595 0.548 GRACILE syndrome chr2 219526444 219526694 0.536 GRACILE syndrome chr2 219526446 219526696 0.536 GRACILE syndrome chr2 234669321 234669571 0.552 Crigler Najjar syndrome Type I chr2 234669332 234669582 0.556 Crigler Najjar syndrome Type I chr2 234669649 234669899 0.440 Crigler Najjar syndrome Type I chr2 234675613 234675863 0.372 Crigler Najjar syndrome Type I chr2 234676394 234676644 0.440 Crigler Najjar syndrome Type I chr2 234676429 234676609 0.456 Crigler Najjar syndrome Type I chr2 234676443 234676693 0.428 Crigler Najjar syndrome Type I chr2 234676780 234677030 0.484 Crigler Najjar syndrome Type I chr3 4490882 4491132 0.396 Multiple sulfatase deficiency chr3 58413682 58413942 0.408 Pyruvate dehydrogenase deficiency PDHB related chr3 58416448 58416708 0.492 Pyruvate dehydrogenase deficiency PDHB related chr3 58416463 58416723 0.488 Pyruvate dehydrogenase deficiency PDHB related chr3 165491155 165491404 0.313 Butyrylcholinesterase deficiency chr3 165548392 165548641 0.402 Butyrylcholinesterase deficiency chr3 182756751 182757011 0.469 3 Methylcrotonyl CoA Carboxylase Deficiency 1 chr3 182756773 182756943 0.506 3 Methylcrotonyl CoA Carboxylase Deficiency 1 chr3 182759343 182759593 0.464 3 Methylcrotonyl CoA Carboxylase Deficiency 1 chr4 52894936 52895196 0.312 Limb girdle muscular dystrophy type 2E chr4 52895808 52896058 0.420 Limb girdle muscular dystrophy type 2E chr4 52895823 52896043 0.427 Limb girdle muscular dystrophy type 2E chr4 100543788 100544038 0.420 Abetalipoproteinemia chr4 123663257 123663507 0.360 Bardet Biedl syndrome 12 chr4 123663787 123664037 0.392 Bardet Biedl syndrome 12 chr4 123663985 123664235 0.408 Bardet Biedl syndrome 12 chr4 123664405 123664655 0.436 Bardet Biedl syndrome 12 chr4 128851825 128852085 0.315 Neuronal ceroid lipofuscinosis MFSD8 related chr4 128859841 128860031 0.300 Neuronal ceroid lipofuscinosis MFSD8 related chr4 146560230 146560480 0.436 Methylmalonic acidemia MMAA related chr4 146560327 146560577 0.396 Methylmalonic acidemia MMAA related chr4 146560432 146560682 0.404 Methylmalonic acidemia MMAA related chr4 146560450 146560700 0.396 Methylmalonic acidemia MMAA related chr4 146560519 146560769 0.372 Methylmalonic acidemia MMAA related chr4 146560579 146560829 0.356 Methylmalonic acidemia MMAA related chr4 146563453 146563703 0.400 Methylmalonic acidemia MMAA related chr4 146563507 146563757 0.436 Methylmalonic acidemia MMAA related chr4 146567088 146567338 0.380 Methylmalonic acidemia MMAA related chr4 146567195 146567445 0.352 Methylmalonic acidemia MMAA related chr4 146576280 146576530 0.476 Methylmalonic acidemia MMAA related chr4 187195222 187195472 0.476 Factor XI deficiency chr4 187201287 187201537 0.512 Factor XI deficiency chr5 70895369 70895629 0.369 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70895399 70895599 0.370 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70898324 70898574 0.408 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70900130 70900350 0.364 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70927898 70928128 0.396 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70930689 70930919 0.352 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70930869 70931139 0.378 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr5 70944892 70945142 0.468 3 Methylcrotonyl CoA Carboxylase Deficiency 2 chr6 51524100 51524284 0.429 Autosomal recessive polycystic kidney disease chr6 51524387 51524577 0.453 Autosomal recessive polycystic kidney disease chr6 51524477 51524726 0.418 Autosomal recessive polycystic kidney disease chr6 51524630 51524870 0.333 Autosomal recessive polycystic kidney disease chr6 51524642 51524812 0.347 Autosomal recessive polycystic kidney disease chr6 51612557 51612806 0.454 Autosomal recessive polycystic kidney disease chr6 51612759 51613008 0.414 Autosomal recessive polycystic kidney disease chr6 51613242 51613431 0.476 Autosomal recessive polycystic kidney disease chr6 51617943 51618148 0.463 Autosomal recessive polycystic kidney disease chr6 51637377 51637626 0.357 Autosomal recessive polycystic kidney disease chr6 51712574 51712767 0.492 Autosomal recessive polycystic kidney disease chr6 51768264 51768495 0.468 Autosomal recessive polycystic kidney disease chr6 51768323 51768528 0.449 Autosomal recessive polycystic kidney disease chr6 51824498 51824736 0.391 Autosomal recessive polycystic kidney disease chr6 51882183 51882367 0.484 Autosomal recessive polycystic kidney disease chr6 51882240 51882489 0.522 Autosomal recessive polycystic kidney disease chr6 51889261 51889460 0.432 Autosomal recessive polycystic kidney disease chr6 51889530 51889709 0.430 Autosomal recessive polycystic kidney disease chr6 51889613 51889863 0.488 Autosomal recessive polycystic kidney disease chr6 51890671 51890920 0.566 Autosomal recessive polycystic kidney disease chr6 51890681 51890931 0.576 Autosomal recessive polycystic kidney disease chr6 51890721 51890971 0.576 Autosomal recessive polycystic kidney disease chr6 51890762 51891012 0.552 Autosomal recessive polycystic kidney disease chr6 51892991 51893208 0.562 Autosomal recessive polycystic kidney disease chr6 51907748 51907997 0.378 Autosomal recessive polycystic kidney disease chr6 51910894 51911114 0.414 Autosomal recessive polycystic kidney disease chr6 51913211 51913461 0.504 Autosomal recessive polycystic kidney disease chr6 51913231 51913481 0.508 Autosomal recessive polycystic kidney disease chr6 51913232 51913482 0.508 Autosomal recessive polycystic kidney disease chr6 51914850 51915048 0.545 Autosomal recessive polycystic kidney disease chr6 51914920 51915167 0.538 Autosomal recessive polycystic kidney disease chr6 51923055 51923235 0.522 Autosomal recessive polycystic kidney disease chr6 51923055 51923268 0.535 Autosomal recessive polycystic kidney disease chr6 51927226 51927475 0.474 Autosomal recessive polycystic kidney disease chr6 51935792 51936041 0.410 Autosomal recessive polycystic kidney disease chr6 51944632 51944881 0.466 Autosomal recessive polycystic kidney disease chr6 51947894 51948003 0.404 Autosomal recessive polycystic kidney disease chr6 80197215 80197465 0.376 Leber congenital amaurosis LCA5 related chr6 80198757 80199007 0.376 Leber congenital amaurosis LCA5 related chr6 80203229 80203479 0.308 Leber congenital amaurosis LCA5 related chr6 80878587 80878757 0.476 Maple syrup urine disease type 1B chr6 80910645 80910835 0.442 Maple syrup urine disease type 1B chr6 80912707 80912957 0.372 Maple syrup urine disease type 1B chr6 80982770 80982970 0.360 Maple syrup urine disease type 1B chr6 81053341 81053571 0.396 Maple syrup urine disease type 1B chr7 92123686 92123936 0.372 Peroxisome biogenesis disorders Zellweger syndrome spectrum PEX1 related chr7 92130708 92130968 0.438 Peroxisome biogenesis disorders Zellweger syndrome spectrum PEX1 related chr7 92130756 92130996 0.404 Peroxisome biogenesis disorders Zellweger syndrome spectrum PEX1 related chr7 92132375 92132625 0.336 Peroxisome biogenesis disorders Zellweger syndrome spectrum PEX1 related chr7 107542660 107542910 0.356 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107555821 107556081 0.331 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107555826 107556076 0.328 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107557622 107557872 0.408 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107557669 107557919 0.432 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107557724 107557974 0.460 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107559423 107559663 0.350 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 107559527 107559787 0.327 Lipoamide Dehydrogenase Deficiency Maplesyrup urine disease Type 3 chr7 117149047 117149297 0.324 Cystic fibrosis chr7 117170889 117171139 0.412 Cystic fibrosis chr7 117171034 117171284 0.380 Cystic fibrosis chr7 117171070 117171270 0.385 Cystic fibrosis chr7 117174290 117174550 0.335 Cystic fibrosis chr7 117174335 117174505 0.341 Cystic fibrosis chr7 117180175 117180361 0.457 Cystic fibrosis chr7 117180203 117180384 0.470 Cystic fibrosis chr7 117188736 117188906 0.388 Cystic fibrosis chr7 117199521 117199770 0.369 Cystic fibrosis chr7 117199607 117199796 0.354 Cystic fibrosis chr7 117227720 117227969 0.349 Cystic fibrosis chr7 117227791 117227987 0.357 Cystic fibrosis chr7 117230384 117230604 0.295 Cystic fibrosis chr7 117232148 117232398 0.372 Cystic fibrosis chr7 117242817 117243027 0.371 Cystic fibrosis chr7 117246704 117246954 0.292 Cystic fibrosis chr7 117267461 117267711 0.344 Cystic fibrosis chr7 117267636 117267886 0.428 Cystic fibrosis chr7 117279890 117280140 0.336 Cystic fibrosis chr7 117282492 117282741 0.394 Cystic fibrosis chr8 19811599 19811869 0.474 Lipoprotein lipase deficiency chr8 77895920 77896170 0.360 Peroxisome biogenesis disorders Zellweger syndrome spectrum PEX2 related chr8 90983285 90983535 0.288 Nijmegen breakage syndrome chr9 6554576 6554826 0.520 Glycine encephalopathy GLDC related chr9 6588298 6588538 0.450 Glycine encephalopathy GLDC related chr9 35075151 35075401 0.484 Fanconi anemia FANCG related chr9 35076448 35076718 0.474 Fanconi anemia FANCG related chr9 35078487 35078717 0.535 Fanconi anemia FANCG related chr9 36217272 36217522 0.520 Inclusion body myopathy type 2 chr9 36219822 36220052 0.517 Inclusion body myopathy type 2 chr9 75671794 75672044 0.392 Choreoacanthocytosis chr9 111656227 111656457 0.287 Familial dysautonomia chr9 111656249 111656419 0.259 Familial dysautonomia chr9 111661976 111662216 0.333 Familial dysautonomia chr9 111662458 111662708 0.444 Familial dysautonomia chr10 56077044 56077304 0.488 Usher syndrome type 1F chr10 56423892 56424142 0.396 Usher syndrome type 1F chr11 5246784 5247034 0.496 Beta thalassemia chr11 5246784 5247034 0.496 Sickle cell disease chr11 5246805 5247019 0.519 Beta thalassemia chr11 5246805 5247019 0.519 Sickle cell disease chr11 5246862 5247067 0.493 Beta thalassemia chr11 5246862 5247067 0.493 Sickle cell disease chr11 5246865 5247108 0.465 Beta thalassemia chr11 5246865 5247108 0.465 Sickle cell disease chr11 5246956 5247165 0.373 Beta thalassemia chr11 5246988 5247238 0.352 Beta thalassemia chr11 5247008 5247258 0.340 Beta thalassemia chr11 5247020 5247210 0.342 Beta thalassemia chr11 5247686 5247936 0.488 Beta thalassemia chr11 5247686 5247936 0.488 Sickle cell disease chr11 5247726 5247976 0.500 Beta thalassemia chr11 5247726 5247976 0.500 Sickle cell disease chr11 5247727 5247977 0.504 Beta thalassemia chr11 5247727 5247977 0.504 Sickle cell disease chr11 5247849 5248045 0.541 Beta thalassemia chr11 5247849 5248045 0.541 Sickle cell disease chr11 5247863 5248085 0.532 Beta thalassemia chr11 5247863 5248085 0.532 Sickle cell disease chr11 5247979 5248198 0.511 Beta thalassemia chr11 5247979 5248198 0.511 Sickle cell disease chr11 5248052 5248302 0.500 Beta thalassemia chr11 5248052 5248302 0.500 Sickle cell disease chr11 5248053 5248303 0.500 Beta thalassemia chr11 5248053 5248303 0.500 Sickle cell disease chr11 5248073 5248323 0.496 Beta thalassemia chr11 5248073 5248323 0.496 Sickle cell disease chr11 5248145 5248333 0.511 Beta thalassemia chr11 5248145 5248333 0.511 Sickle cell disease chr11 5248265 5248515 0.536 Beta thalassemia chr11 61161308 61161568 0.400 Joubert syndrome Type 2 chr12 48524042 48524312 0.459 Glycogen storage disease type 7 chr12 48525048 48525308 0.481 Glycogen storage disease type 7 chr12 48526572 48526822 0.552 Glycogen storage disease type 7 chr12 65116744 65116994 0.452 Sanfilippo syndrome type D Mucopolysaccharidosis III D chr12 65122677 65122917 0.438 Sanfilippo syndrome type D Mucopolysaccharidosis III D chr12 65122717 65122887 0.435 Sanfilippo syndrome type D Mucopolysaccharidosis III D chr12 65130690 65130950 0.446 Sanfilippo syndrome type D Mucopolysaccharidosis III D chr12 103234177 103234426 0.470 Phenylketonurea chr12 103234235 103234340 0.505 Phenylketonurea chr12 103237341 103237591 0.512 Phenylketonurea chr12 103237398 103237647 0.478 Phenylketonurea chr12 103237926 103238175 0.406 Phenylketonurea chr12 103240539 103240788 0.478 Phenylketonurea chr12 103245348 103245598 0.500 Phenylketonurea chr12 103245355 103245604 0.498 Phenylketonurea chr12 103245396 103245646 0.456 Phenylketonurea chr12 103246427 103246677 0.496 Phenylketonurea chr12 103246447 103246697 0.512 Phenylketonurea chr12 103246468 103246718 0.548 Phenylketonurea chr12 103246529 103246778 0.538 Phenylketonurea chr12 103248878 103249127 0.454 Phenylketonurea chr12 103248915 103249165 0.464 Phenylketonurea chr12 103260323 103260520 0.467 Phenylketonurea chr12 103260324 103260520 0.464 Phenylketonurea chr12 103260384 103260490 0.453 Phenylketonurea chr12 103288527 103288776 0.442 Phenylketonurea chr12 103306591 103306840 0.345 Phenylketonurea chr13 41373103 41373363 0.542 Ornithine translocase deficiency HHH Syndrome chr13 41381416 41381666 0.460 Ornithine translocase deficiency HHH Syndrome chr15 40707508 40707758 0.556 Isovaleric acidemia chr15 40707528 40707778 0.552 Isovaleric acidemia chr15 40707529 40707779 0.552 Isovaleric acidemia chr15 72638766 72639016 0.532 Tay Sachs disease chr15 72638773 72639023 0.548 Tay Sachs disease chr15 72638813 72639063 0.520 Tay Sachs disease chr15 72638834 72639084 0.500 Tay Sachs disease chr15 72638860 72639056 0.515 Tay Sachs disease chr15 72638888 72639158 0.500 Tay Sachs disease chr15 72640258 72640508 0.544 Tay Sachs disease chr15 72642729 72642989 0.523 Tay Sachs disease chr15 72642789 72643049 0.535 Tay Sachs disease chr15 72642795 72643055 0.527 Tay Sachs disease chr15 72642830 72643090 0.515 Tay Sachs disease chr15 89753880 89754130 0.580 Retinal Dystrophy RLBP1 related Bothnia retinal dystrophy chr15 89753895 89754145 0.564 Retinal Dystrophy RLBP1 related Bothnia retinal dystrophy chr15 89753901 89754151 0.564 Retinal Dystrophy RLBP1 related Bothnia retinal dystrophy chr15 89753920 89754130 0.567 Retinal Dystrophy RLBP1 related Bothnia retinal dystrophy chr16 3293218 3293428 0.524 Familial mediterranean fever chr16 3293282 3293532 0.532 Familial mediterranean fever chr16 3293284 3293534 0.536 Familial mediterranean fever chr16 3293312 3293561 0.558 Familial mediterranean fever chr16 3293322 3293572 0.548 Familial mediterranean fever chr16 3293404 3293654 0.516 Familial mediterranean fever chr16 3297026 3297276 0.584 Familial mediterranean fever chr16 3297032 3297302 0.589 Familial mediterranean fever chr16 3297056 3297276 0.582 Familial mediterranean fever chr16 3297061 3297271 0.576 Familial mediterranean fever chr16 3297076 3297256 0.572 Familial mediterranean fever chr16 3297081 3297251 0.571 Familial mediterranean fever chr17 435963 436203 0.433 Pontocerebellar hypoplasia Type 2E chr17 435973 436193 0.427 Pontocerebellar hypoplasia Type 2E chr17 465619 465869 0.476 Pontocerebellar hypoplasia Type 2E chr17 3386686 3386896 0.386 canavan disease chr17 3397597 3397807 0.343 canavan disease chr17 3402201 3402371 0.459 canavan disease chr17 19566518 19566768 0.352 Sjogren Larsson syndrome chr17 19574999 19575249 0.404 Sjogren Larsson syndrome chr6 51747804 51748053 0.382 Autosomal recessive polycystic kidney disease chr12 103271260 103271440 0.528 Phenylketonurea chr12 103271262 103271432 0.506 Phenylketonurea chr12 103271265 103271435 0.512 Phenylketonurea chr12 103271270 103271440 0.524 Phenylketonurea chr12 103271293 103271492 0.492 Phenylketonurea chr2 219527213 219527463 0.588 GRACILE syndrome chr2 219527776 219528026 0.572 GRACILE syndrome chr2 234668852 234669102 0.564 GRACILE syndrome chr16 3304532 3304735 0.714 Familial mediterranean fever chr16 3304566 3304735 0.704 Familial mediterranean fever chr16 3304577 3304735 0.703 Familial mediterranean fever chr16 3304534 3304699 0.745 Familial mediterranean fever chr16 3304534 3304718 0.728 Familial mediterranean fever chr16 3304601 3304844 0.630 Familial mediterranean fever

DETAILED DESCRIPTION

The invention pertains to a method for analyzing genetic abnormalities that involves hybridization-based enrichment of selected target regions across the human genome in a multiplexed panel assay, followed by quantification, coupled with a novel bioinformatics and mathematical analysis pipeline. An overview of the method is shown schematically in FIG. 1.

In-solution hybridization enrichment has been used in the past to enrich specific regions of interest prior to sequencing (see e.g., Meyer, M and Kirchner, M. (2010) Cold Spring Harb. Protoc. 2010(6):pdbprot5448; Liao, G. J. et al. (2012) PLoS One 7:e38154; Maricic, T. et al. (2010) PLoS One 5:e14004; Tewhey, R. et al. (2009) Genome Biol. 10:R116; Tsangaras, K. et al. (2014) PLoS One 9:e109101; PCT Publication WO 2016/189388; US Patent Publication 2016/0340733; Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855). However, for the methods of the invention, the target sequences (referred to as TArget Capture Sequences, or TACS) used to enrich for specific regions of interest have been optimized for maximum efficiency, specificity and accuracy and, furthermore, allow for assessment of fetal risk of inherited genetic conditions, as well as assessment of fetal risk of genetic abnormalities.

Furthermore, in certain embodiments, the TACS used in the methods are families of TACS, comprising a plurality of members that bind to the same genomic sequence but with differing start and/or stop positions, such that enrichment of the genomic sequences of interest is significantly improved compared to use of a single TACS binding to the genomic sequence. The configuration of such families of TACS is illustrated schematically in FIG. 3, showing that the different start and/or stop positions of the members of the TACS family when bound to the genomic sequence of interest results in a staggered binding pattern for the family members.

The use of families of TACS with the TACS pool that bind to each target sequence of interest, as compared to use of a single TACS within the TACS pool that binds to each target sequence of interest, significantly increases enrichment for the target sequences of interest, as evidenced by a greater than 50% average increase in read-depth for the family of TACS versus a single TACS. Comparison of use of a family of TACS versus a single TACS, and the significantly improved read-depth that was observed, is described in detail in Example 5.

Parental Carrier Status and Fetal Risk of Inheritance of Genetic Conditions

The methods of the disclosure can be used to determine parental carrier status of inheritable genetic abnormalities associated with genetic conditions (e.g., maternal carrier status and, if necessary based on the maternal status, also paternal carrier status), and from this information the fetal risk of inheriting the genetic condition can be determined. An exemplification of this method is described in Example 6. Accordingly, in another aspect, the invention pertains to a method of determining fetal risk of inheriting a genetic condition, the method comprising:

(a) preparing a sequencing library from a sample comprising maternal and fetal DNA;

(b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of sequences that bind to genomic sequences of interest including variant allele loci of interest associated with different genetic conditions;

(c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;

(d) amplifying and sequencing the enriched library;

(e) performing statistical analysis on the enriched library sequences to thereby determine maternal carrier status at the loci of interest associated with different genetic conditions, wherein for a sample with a positive maternal carrier status, the method further comprises:

(f) obtaining a paternal DNA sample and performing steps (a)-(e) on the paternal DNA sample to determine paternal carrier status at those loci for which there is a positive maternal carrier status; and

(g) determining fetal risk of inheriting a genetic condition based on maternal carrier status and, when (f) is performed, paternal carrier status.

In one embodiment,

-   -   (i) each member sequence within the pool of TACS is between         100-500 base pairs in length, each member sequence having a 5′         end and a 3′ end;     -   (ii) each member sequence binds to the same locus of interest at         least 50 base pairs away, on both the 5′ end and the 3′ end,         from regions harboring Copy Number Variations (CNVs), Segmental         duplications or repetitive DNA elements; and     -   (iii) the GC content of the pool of TACS is between 19% and 80%,         as determined by calculating the GC content of each member         within the pool of TACS.

The method of carrier determination and fetal inheritance risk can be combined with detecting chromosomal and structural abnormalities in the fetal DNA, as described in Examples 1-4, in the same sample containing the maternal and fetal DNA (e.g., the maternal plasma sample). That is, maternal carrier determination and detection of fetal chromosomal abnormalities can be assessed simultaneously using the same sample (e.g., maternal plasma sample) through the inclusion of the appropriate TACS in the pool of TACS used in the method. Accordingly, in one embodiment of the method, the pool of TACS further comprises sequences that bind to chromosomes of interest for detecting fetal chromosomal abnormalities and step (e) further comprises performing statistical analysis on the enriched library sequences to thereby determine fetal risk of a chromosomal abnormality at the chromosome of interest. In one embodiment, the chromosomal abnormality is an aneuploidy, such as a trisomy or a monosomy. Other types of chromosomal abnormalities that can be detected are described herein. In one embodiment, the chromosomes of interest include chromosomes 13, 18, 21, X and Y.

For determining parental carrier status, TACS are designed to bind to variant allele loci of interest that are associated with inheritable genetic conditions. In one embodiment, the sample (e.g., maternal plasma sample) is screened to determine maternal carrier status for a plurality of variant alleles, wherein each family of TACS binds to a variant allele locus associated with a genetic condition. In one embodiment the variant allele loci of interest are associated with genetic conditions selected from the group consisting of, but not limited to, Achondroplasia, Alpha-1 Antitrypsin Deficiency, Antiphospholipid Syndrome, Autism, Autosomal Dominant Polycystic Kidney Disease, Autosomal Recessive Polycystic Kidney Disease, Inheritable Breast Cancer Gene, Charcot-Marie-Tooth, Inheritable Colon Cancer Gene, Crohn's Disease, Cystic Fibrosis, Dercum Disease, Duane Syndrome, Duchenne Muscular Dystrophy, Factor V Leiden Thrombophilia, Familial Hypercholesterolemia, Familial Mediterranean Fever, Fragile X Syndrome, Gaucher Disease, Hemochromatosis, Hemophilia, Holoprosencephaly, Huntington's Disease, Marfan Syndrome, Myotonic Dystrophy, Neurofibromatosis, Noonan Syndrome, Osteogenesis Imperfecta, Phenylketonuria, Poland Anomaly, Porphyria, Prostate Cancer, Retinitis Pigmentosa, Severe Combined Immunodeficiency (SCID), Sickle Cell Disease, Spinal Muscular Atrophy, Tay-Sachs, Thalassemia, WAGR Syndrome, Wilson Disease, and combinations thereof.

For samples in which the mother has been determined to be a carrier of a variant allele associated with an inheritable genetic condition (positive maternal carrier status), a sample of paternal DNA can also be assessed using the method to thereby determine the parental carrier status, thus allowing for calculation of the fetal risk of inheritance of the genetic condition. Accordingly, in one embodiment, the method further comprises, for a sample with a positive maternal carrier status, obtaining a paternal DNA sample and performing steps (a)-(e) of the above-described method on the paternal DNA sample to determine paternal carrier status, to thereby compute a fetal risk score for inheriting the genetic condition.

A non-limiting example of computation of a fetal risk score is described in Example 6, in which both the maternal sample and the paternal sample are carriers for a recessive disease-associated allele (heterozygous for the recessive disease-associated allele) and thus the fetus is calculated to have a 25% chance of inheriting a homozygous recessive disease-associated genotype. Alternative fetal risk scores based on the maternal and/or paternal carrier status and the recessiveness or dominance of the disease-associated allele can readily be calculated by the ordinarily skilled artisan using Mendelian Genetics reasoning well established in the art.

In one embodiment, the pool of TACS comprises a plurality of TACS families, wherein each member of a TACS family binds to the same target sequence of interest but with different start and/or stop positions on the sequence with respect to a reference coordinate system (i.e., binding of TACS family members to the target sequence is staggered) to thereby enrich for target sequences of interest, followed by massive parallel sequencing and statistical analysis of the enriched population. The use of families of TACS with the TACS pool that bind to each target sequence of interest, as compared to use of a single TACS within the TACS pool that binds to each target sequence of interest, significantly increases enrichment for the target sequences of interest, as evidenced by a greater than 50% average increase in read-depth for the family of TACS versus a single TACS.

Accordingly, in one embodiment, the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest.

Thus, in another aspect, the invention pertains to a method of determining fetal risk of inheriting a genetic condition, the method comprising:

(a) preparing a sequencing library from a sample comprising maternal and fetal DNA;

(b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of TACS families directed to variant allele loci of interest associated with different genetic conditions, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same variant allele loci of interest but has different start and/or stop positions with respect to a reference coordinate system for the variant allele loci of interest;

(c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library;

(d) amplifying and sequencing the enriched library; and

(e) performing statistical analysis on the enriched library sequences to thereby determine maternal carrier status at the loci of interest associated with different genetic conditions, wherein for a sample with a positive maternal carrier status, the method further comprises:

(f) obtaining a paternal DNA sample and performing steps (a)-(e) on the paternal DNA sample to determine paternal carrier status for those diseases in which there is a positive maternal carrier status; and

(g) determining fetal risk of inheriting a genetic condition based on maternal carrier status and, when (f) is performed, paternal carrier status.

In one embodiment:

-   -   (i) each member sequence within each TACS family is between         100-500 base pairs in length, each member sequence having a 5′         end and a 3′ end;     -   (ii) each member sequence binds to the same variant allele locus         of interest at least 50 base pairs away, on both the 5′ end and         the 3′ end, from regions harboring Copy Number Variations         (CNVs), Segmental duplications or repetitive DNA elements; and     -   (iii) the GC content of the pool of TACS is between 19% and 80%,         as determined by calculating the GC content of each member         within each family of TACS.

The TACS-enrichment based method of the disclosure can be used in the detection of a wide variety of genetic abnormalities. In one embodiment, the genetic abnormality is a chromosomal aneuploidy (such as a trisomy, a partial trisomy or a monosomy). In other embodiments, the genomic abnormality is a structural abnormality, including but not limited to copy number changes including microdeletions and microduplications, insertions, translocations, inversions and small-size mutations including point mutations and mutational signatures. In another embodiment, the genetic abnormality is a chromosomal mosaicism.

TArget Capture Sequence Design

As used herein, the term “TArget Capture Sequences” or “TACS” refers to short DNA sequences that are complementary to the region(s) of interest on a genomic sequence(s) of interest (e.g., chromosome(s) of interest) and which are used as “bait” to capture and enrich the region of interest from a large library of sequences, such as a whole genomic sequencing library prepared from a biological sample. A pool of TACS is used for enrichment wherein the sequences within the pool have been optimized with regard to: (i) the length of the sequences; (ii) the distribution of the TACS across the region(s) of interest; and (iii) the GC content of the TACS. The number of sequences within the TACS pool (pool size) has also been optimized.

It has been discovered that TACS having a length of 100-500 base pairs are optimal to maximize enrichment efficiency. In various other embodiments, each sequence within the pool of TACS is between 150-260 base pairs, 100-200 base pairs, 200-260 base pairs, 100-350 base pairs in length, or 100-500 base pairs in length. In preferred embodiments, the length of the TACS within the pool is at least 250 base pairs, or is 250 base pairs or is 260 base pairs or is 280 base pairs. It will be appreciated by the ordinarily skilled artisan that a slight variation in TACS size typically can be used without altering the results (e.g., the addition or deletion of a few base pairs on either end of the TACS); accordingly, the base pair lengths given herein are to be considered “about” or “approximate”, allowing for some slight variation (e.g., 1-5%) in length. Thus, for example, a length of “250 base pairs” is intended to refer to “about 250 base pairs” or “approximately 250 base pairs”, such that, for example, 248 or 252 base pairs is also encompassed.

The distribution of the TACS across each region or chromosome of interest has been optimized to avoid high copy repeats, low copy repeats and copy number variants, while at the same time also being able to target informative single nucleotide polymorphisms (SNPs) in order to enable both aneuploidy, or structural copy number change detection, and fetal fraction (ff) estimation. Accordingly, each sequence within the TACS pool is designed such that the 5′ end and the 3′ end are each at least 50 base pairs away from regions in the genome that are known to harbour one or more of the following genomic elements: Copy Number Variations (CNVs), Segmental duplications and/or repetitive DNA elements (such as transposable elements or tandem repeat areas). In various other embodiments, each sequence within the TACS pool is designed such that the 5′ end and the 3′ end are each at least 50, 100, 150, 200, 250, 300, 400 or 500 base pairs away from regions in the genome that are known to harbour one or more of the aforementioned elements.

The term “Copy Number Variations” is a term of art that refers to a form of structural variation in the human genome in which there can be alterations in the DNA of the genome in different individuals that can result in a fewer or greater than normal number of a section(s) of the genome in certain individuals. CNVs correspond to relatively large regions of the genome that may be deleted (e.g., a section that normally is A-B-C-D can be A-B-D) or may be duplicated (e.g., a section that normally is A-B-C-D can be A-B-C-C-D). CNVs account for roughly 13% of the human genome, with each variation ranging in size from about 1 kilobase to several megabases in size.

The term “Segmental duplications” (also known as “low-copy repeats”) is also a term of art that refers to blocks of DNA that range from about 1 to 400 kilobases in length that occur at more than one site within the genome and typically share a high level (greater than 90%) of sequence identity. Segmental duplications are reviewed in, for example, Eichler. E. E. (2001) Trends Genet. 17:661-669.

The term “repetitive DNA elements” (also known as “repeat DNA” or “repeated DNA”) is also a term of art that refers to patterns of DNA that occur in multiple copies throughout the genome. The term “repetitive DNA element” encompasses terminal repeats, tandem repeats and interspersed repeats, including transposable elements. Repetitive DNA elements in NGS is discussed further in, for example, Todd, J. et al. (2012) Nature Reviews Genet. 13:36-46.

The TACS are designed with specific GC content characteristics in order to minimize data GC bias and to allow a custom and innovative data analysis pipeline. It has been determined that TACS with a GC content of 19-80% achieve optimal enrichment and perform best with cell free fetal DNA. Within the pool of TACS, different sequences can have different % GC content, although to be selected for inclusion with the pool, the % GC content of each sequence is chosen as between 19-80%, as determined by calculating the GC content of each member within the pool of TACS or within each family of TACS. That is, every member within the pool or within each family of TACS in the pool has a % GC content within the given percentage range (e.g., between 19-80% GC content).

In some instances, the pool of TACS (e.g., each member within each family of TACS) may be chosen so as to define a different % GC content range, deemed to be more suitable for the assessment of specific genetic abnormalities. Non-limiting examples of various % GC content ranges, can be between 19% and 80%, or between 19% and 79%, or between 19% and 78%, or between 19% and 77%, or between 19% and 76%, or between 19% and 75%, or between 19% and 74%, or between 19% and 73%, or between 19% and 72%, or between 19% and 71%, or between 19% and 70%, or between 19% and 69%, or between 19% and 68%, or between 19% and 67%, or between 19% and 66%, or between 19% and 65%, or between 19% and 64%, or between 19% and 63%, or between 19% and 62%, or between 19% and 61%, or between 19% and 60%, or between 19% and 59%, or between 19% and 58%, or between 19% and 57%, or between 19% and 56%, or between 19% and 55%, or between 19% and 54%, or between 19% and 53%, or between 19% and 52%, or between 19% and 51%, or between 19% and 50%, or between 19% and 49%, or between 19% and 48%, or between 19% and 47%, or between 19% and 46%, or between 19% and 45%, or between 19% and 44%, or between 19% and 43%, or between 19% and 42%, or between 19% and 41%, or between 19% and 40%.

As described in further detail below with respect to one embodiment of the data analysis, following amplification and sequencing of the enriched sequences, the test loci and reference loci can then be “matched” or grouped together according to their % GC content (e.g., test loci with a % GC content of 40% is matched with reference loci with a % GC content of 40%). It is appreciated that the % GC content matching procedure may allow slight variation in the allowed matched % GC range. A non-limiting instance, and with reference to the previously described example in text, a test locus with % GC content of 40% could be matched with reference loci of % GC ranging from 39-41%, thereby encompassing the test locus % GC within a suitable range.

To prepare a pool of TACS having the optimized criteria set forth above with respect to size, placement within the human genome and % GC content, both manual and computerized analysis methods known in the art can be applied to the analysis of the human reference genome. In one embodiment, a semi-automatic method is implemented where regions are firstly manually designed based on the human reference genome build 19 (hg19) ensuring that the aforementioned repetitive regions are avoided and subsequently are curated for GC-content using software that computes the % GC-content of each region based on its coordinates on the human reference genome build 19 (hg19). In another embodiment, custom-built software is used to analyze the human reference genome in order to identify suitable TACS regions that fulfil certain criteria, such as but not limited to, % GC content, proximity to repetitive regions and/or proximity to other TACS.

The number of TACS in the pool has been carefully examined and adjusted to achieve the best balance between result robustness and assay cost/throughput. The pool typically contains at least 800 or more TACS, but can include more, such as 1500 or more TACS, 2000 or more TACS or 2500 or more TACS or 3500 or more TACS or 5000 or more TACS. It has been found that an optimal number of TACS in the pool is 5000. It will be appreciated by the ordinarily skilled artisan that a slight variation in pool size typically can be used without altering the results (e.g., the addition or removal of a small number of TACS); accordingly, the number sizes of the pool given herein are to be considered “about” or “approximate”, allowing for some slight variation (e.g., 1-5%) in size. Thus, for example, a pool size of “1600 sequences” is intended to refer to “about 1600 sequences” or “approximately 1600 sequences”, such that, for example, 1590 or 1610 sequences is also encompassed.

In view of the foregoing, in another aspect, the invention provides a method for preparing a pool of TACS for use in the method of the invention for detecting risk of a chromosomal and/or other genetic abnormality, wherein the method for preparing the pool of TACS comprises: selecting regions in one or more chromosomes of interest having the criteria set forth above (e.g., at least 50 base pairs away on either end from the aforementioned repetitive sequences and a GC content of between 19% and 80%, as determined by calculating the GC content of each member within each family of TACS), preparing primers that amplify sequences that hybridize to the selected regions, and amplifying the sequences, wherein each sequence is 100-500 base pairs in length.

For use in the methods of the disclosure, the pool of TACS typically is fixed to a solid support, such as beads (such as magnetic beads) or a column. In one embodiment, the pool of TACS are labeled with biotin and are bound to magnetic beads coated with a biotin-binding substance, such as streptavidin or avidin, to thereby fix the pool of TACS to a solid support. Other suitable binding systems for fixing the pool of TACS to a solid support (such as beads or column) are known to the skilled artisan and readily available in the art. When magnetic beads are used as the solid support, sequences that bind to the TACS affixed to the beads can be separated magnetically from those sequences that do not bind to the TACS.

Families of TACS

In one embodiment, the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest.

Each TACS family comprises a plurality of members that bind to the same genomic sequence of interest but having different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest. Typically, the reference coordinate system that is used for analyzing human genomic DNA is the human reference genome built hg19, which is publically available in the art, but other reference coordinate systems can be used as well. Alternatively, the reference coordinate system can be an artificially created genome based on built hg19 that contains only the genomic sequences of interest. Exemplary non-limiting examples of start/stop positions for TACS that bind to chromosome 13, 18, 21, X or Y are shown in FIG. 2.

Each TACS family comprises at least 2 members that bind to the same genomic sequence of interest. In various embodiments, each TACS family comprises at least 2 member sequences, or at least 3 member sequences, or at least 4 member sequences, or at least 5 member sequences, or at least 6 member sequences, or at least 7 member sequences, or at least 8 member sequence, or at least 9 member sequences, or at least 10 member sequences. In various embodiments, each TACS family comprises 2 member sequences, or 3 member sequences, or 4 member sequences, or 5 member sequences, or 6 member sequences, or 7 member sequences, or 8 member sequences, or 9 member sequences, or 10 member sequences. In various embodiments, the plurality of TACS families comprises different families having different numbers of member sequences. For example, a pool of TACS can comprise one TACS family that comprises 3 member sequences, another TACS family that comprises 4 member sequences, and yet another TACS family that comprises 5 member sequences, and the like. In one embodiment, a TACS family comprises 3-5 member sequences. In another embodiment, the TACS family comprises 4 member sequences.

The pool of TACS comprises a plurality of TACS families. Thus, a pool of TACS comprises at least 2 TACS families. In various embodiments, a pool of TACS comprises at least 3 different TACS families, or at least 5 different TACS families, or at least 10 different TACS families, or at least 50 different TACS families, or at least 100 different TACS families, or at least 500 different TACS families, or at least 1000 different TACS families, or at least 2000 TACS families, or at least 4000 TACS families, or at least 5000 TACS families.

Each member within a family of TACS binds to the same genomic region of interest but with different start and/or stop positions, with respect to a reference coordinate system for the genomic sequence of interest, such that the binding pattern of the members of the TACS family is staggered (see FIG. 3). In various embodiments, the start and/or stop positions are staggered by at least 3 base pairs, or at least 4 base pairs, or at least 5 base pairs, or at least 6 base pairs, or at least 7 base pairs, or at least 8 base pairs, or at least 9 base pairs, or at least 10 base pairs, or at least 15 base pairs, or at least 20 base pairs, or at least 25 base pairs. Typically, the start and/or stop positions are staggered by 5-10 base pairs. In one embodiment, the start and/or stop positions are staggered by 5 base pairs. In another embodiment, the start and/or stop positions are staggered by 10 base pairs.

Sample Collection and Preparation

The methods of the invention can be used with a variety of biological samples. In one embodiment for NIPT, the sample is a mixed sample that contains both maternal DNA and fetal DNA (e.g., cell-free fetal DNA (cffDNA)), such as a maternal plasma sample obtained from maternal peripheral blood. Typically for mixed maternal/fetal DNA samples, the sample is a maternal plasma sample, although other tissue sources that contain both maternal and fetal DNA can be used. Maternal plasma can be obtained from a peripheral whole blood sample from a pregnant woman and the plasma can be obtained by standard methods. As little as 2-4 ml of plasma is sufficient to provide suitable DNA material for analysis according to the method of the disclosure. Total cell free DNA can then be extracted from the sample using standard techniques, non-limiting examples of which include a Qiasymphony protocol (Qiagen) suitable for free fetal DNA isolation or any other manual or automated extraction method suitable for cell free DNA isolation.

For the biological sample preparation, typically cells are lysed and DNA is extracted using standard techniques known in the art, a non-limiting example of which is the Qiasymphony (Qiagen) protocol.

Following isolation, the cell free DNA of the sample is used for sequencing library construction to make the sample compatible with a downstream sequencing technology, such as Next Generation Sequencing. Typically this involves ligation of adapters onto the ends of the cell free DNA fragments, followed by amplification. Sequencing library preparation kits are commercially available. A non-limiting exemplary protocol for sequencing library preparation is described in detail in Example 1.

Enrichment by TACS Hybridization

The region(s) of interest on the chromosome(s) of interest is enriched by hybridizing the pool of TACS to the sequencing library, followed by isolation of those sequences within the sequencing library that bind to the TACS. To facilitate isolation of the desired, enriched sequences, typically the TACS sequences are modified in such a way that sequences that hybridize to the TACS can be separated from sequences that do not hybridize to the TACS. Typically, this is achieved by fixing the TACS to a solid support. This allows for physical separation of those sequences that bind the TACS from those sequences that do not bind the TACS. For example, each sequence within the pool of TACS can be labeled with biotin and the pool can then be bound to beads coated with a biotin-binding substance, such as streptavidin or avidin. In a preferred embodiment, the TACS are labeled with biotin and bound to streptavidin-coated magnetic beads. The ordinarily skilled artisan will appreciate, however, that other affinity binding systems are known in the art and can be used instead of biotin-streptavidin/avidin. For example, an antibody-based system can be used in which the TACS are labeled with an antigen and then bound to antibody-coated beads. Moreover, the TACS can incorporate on one end a sequence tag and can be bound to a solid support via a complementary sequence on the solid support that hybridizes to the sequence tag. Furthermore in addition to magnetic beads, other types of solid supports can be used, such as polymer beads and the like.

In certain embodiments, the members of the sequencing library that bind to the pool of TACS are fully complementary to the TACS. In other embodiments, the members of the sequencing library that bind to the pool of TACS are partially complementary to the TACS. For example, in certain circumstances it may be desirable to utilize and analyze data that are from DNA fragments that are products of the enrichment process but that do not necessarily belong to the genomic regions of interest (i.e. such DNA fragments could bind to the TACS because of part homologies (partial complementarity) with the TACS and when sequenced would produce very low coverage throughout the genome in non-TACS coordinates).

Following enrichment of the sequence(s) of interest using the TACS, thereby forming an enriched library, the members of the enriched library are eluted from the solid support and are amplified and sequenced using standard methods known in the art. Next Generation Sequencing is typically used, although other sequencing technologies can also be employed, which provides very accurate counting in addition to sequence information. To detect genetic abnormalities, such as but not limited to, aneuploidies or structural copy number changes requires very accurate counting and NGS is a type of technology that enables very accurate counting. Accordingly, for the detection of genetic abnormalities, such as but not limited to, aneuploidies or structural copy number changes, other accurate counting methods, such as digital PCR and microarrays can also be used instead of NGS. Non-limiting exemplary protocols for amplification and sequencing of the enriched library are described in detail in Example 3.

Data Analysis

The information obtained from the sequencing of the enriched library can be analyzed using an innovative biomathematical/biostatistical data analysis pipeline. Details of an exemplary analysis using this pipeline are described in depth in Example 4, and in further detail below. Alternative data analysis approaches for different purposes are also provided herein.

The analysis pipeline described in Example 4 exploits the characteristics of the TACS, and the high-efficiency of the target capture enables efficient detection of aneuploidies or structural copy number changes, as well as other types of genetic abnormalities. In the analysis, first the sample's sequenced DNA fragments are aligned to the human reference genome. QC metrics are used to inspect the aligned sample's properties and decide whether the sample is suitable to undergo classification. These QC metrics can include, but are not limited to, analysis of the enrichment patterns of the loci of interest, such as for example the overall sequencing depth of the sample, the on-target sequencing output of the sample, TACS performance, GC bias expectation, fraction of interest quantification. For determining the risk of a chromosomal abnormality in the fetal DNA of the sample, an innovative algorithm is applied. The steps of the algorithm include, but are not limited to, removal of inadequately sequenced loci, read-depth and fragment-size information extraction at TACS-specific coordinates, genetic (GC-content) bias alleviation and ploidy status classification.

Ploidy status determination is achieved using one or more statistical methods, non-limiting examples of which include a t-test method, a bootstrap method, a permutation test and/or a binomial test of proportions and/or segmentation-based methods and/or combinations thereof. It will be appreciated by the ordinarily skilled artisan that the selection and application of tests to be included in ploidy status determination is based on the number of data points available. As such, the suitability of each test is determined by various factors such as, but not limited to, the number of TACS utilized and the respective application for GC bias alleviation, if applicable. Thus, the aforementioned methods are to be taken as examples of the types of statistical analysis that may be employed and are not the only methods suitable for the determination of ploidy status. Typically, the statistical method results in a score value for the mixed sample and risk of the chromosomal abnormality in the fetal DNA is detected when the score value for the mixed sample is above a reference threshold value.

In particular, one aspect of the statistical analysis involves quantifying and alleviating GC-content bias. In addition to the challenge of detecting small signal changes in fetal DNA in the mixed sample, and/or other components of DNA of interest part of a mixed sample (for example, but not limited to, additional or less genetic material from certain chromosomal regions), the sequencing process itself introduces certain biases that can obscure signal detection. One such bias is the preferential sequencing/amplification of genetic regions based on their GC-content. As such, certain detection methods, such as but not limited to, read-depth based methods, need to account for such bias when examining sequencing data. Thus, the bias in the data needs to be quantified and, subsequently, suitable methods are applied to account for it such that genetic context dependencies cannot affect any statistical methods that may be used to quantify fetal genetic abnormality risk.

For example, one method of quantifying the GC-content bias is to use a locally weighted scatterplot smoothing (LOESS) technique on the sequencing data. Each targeted locus may be defined by its sequencing read-depth output and its' GC-content. A line of best fit through these two variables, for a large set of loci, provides an estimate of the expected sequencing read-depth given the GC-content. Once this GC-bias quantification step is completed, the next step is to use this information to account for possible biases in the data. One method is to normalize the read-depth of all loci by their expected read-depth (based on each locus' GC-content). In principle, this unlinks the read-depth data from their genetic context and makes all data comparable. As such, data that are retrieved from different GC-content regions, such as for example, but not limited, to different chromosomes, can now be used in subsequent statistical tests for detection of any abnormalities. Thus, using the LOESS procedure, the GC bias is unlinked from the data prior to statistical testing. In one embodiment, the statistical analysis of the enriched library sequences comprises alleviating GC bias using a LOESS procedure.

In an alternative preferred embodiment, the GC-content bias is quantified and alleviated by grouping together loci of similar (matching) GC-content. Thus, conceptually this method for alleviating GC-content bias comprises of three steps, as follows:

1) identification and calculation of GC-content in the TACS;

2) alleviation/accounting of GC-content bias using various matching/grouping procedures of the TACS; and

3) calculation of risk of any genetic abnormalities that may be present in the fetus utilizing statistical and mathematical methods on datasets produced from step 2.

For the t-test method, the dataset is split into two groups; the test loci and the reference loci. For each group, subsets of groups are created where loci are categorized according to their GC-content as illustrated in a non-limiting example in the sample Table 1 below:

TABLE 1 GC Reference loci read-depth Test loci read-depth 40% x₁ ⁴⁰, x₂ ⁴⁰, . . . ,x_(nx40) ⁴⁰ y₁ ⁴⁰, y₂ ⁴⁰, . . . , y_(ny40) ⁴⁰ 41% x₁ ⁴¹, x₂ ⁴¹, . . . ,x_(nx41) ⁴¹ y₁ ⁴¹, y₂ ⁴¹, . . . , y_(ny41) ⁴¹ 42% x₁ ⁴², x₂ ⁴², . . . ,x_(nx42) ⁴² y₁ ⁴², y₂ ⁴², . . . , y_(ny42) ⁴² . . . . . . . . . It is appreciated by the ordinarily skilled artisan that subgroup creation may involve encompassing a range of appropriate GC-content and/or a subset of loci that are defined by a given GC-content and/or GC-content range. Accordingly, the % GC content given in the non-limiting example of Table 1 are to be considered “about” or “approximate”, allowing for some slight variation (e.g., 1-2%). Thus, for example, a % GC content of “40%” is intended to refer to “about 40%” or “approximately 40%”, such that, for example, “39%-41%” GC-content loci may also be encompassed if deemed appropriate. Hence, when referring to a particular GC-content it is understood that the reference and test loci subgroups may comprise of any number of loci related to a particular % GC content and/or range.

Subsequently, for each GC-content subgroup, a representative read-depth is calculated. A number of methods may be utilized to choose this such as, but not limited to, the mean, median or mode of each set. Thus, two vectors of representative read-depth are created where one corresponds to the reference loci and the other to the test loci (e.g., Xm, Ym). In one embodiment, the two vectors may be tested against each other to identify significant differences in read-depth. In another embodiment, the difference of the two vectors may be used to assess if there are significant discrepancies between the test and reference loci. The sample is attributed the score of the test.

For statistical analysis using a bootstrap approach, the dataset is split into two groups, the test loci and the reference loci. The GC-content of each locus is then calculated. Then the following procedure is performed:

A random locus is selected from the reference loci; its read-depth and GC-content are recorded. Subsequently, a random locus from the test loci is selected, with the only condition being that its' GC-content is similar to that of the reference locus. Its read-depth is recorded. It is appreciated by the ordinarily skilled artisan that GC-content similarity may encompass a range of suitable GC-content. As such, referral to a specific % GC content may be considered as “approximate” or “proximal” or “within a suitable range” (e.g., 1%-2%) encompassing the specific % GC content under investigation. Thus, a reference-test locus pair of similar GC-content is created. The difference of the reference-test pair is recorded, say E1. The loci are then replaced to their respective groups. This process is repeated until a bootstrap sample of the same size as the number of test TACS present is created. A representative read-depth of the bootstrap sample is estimated, say E_mu, and recorded. A number of methods may be utilized to do so, such as but not limited to, the mean, mode or median value of the vector, and/or multiples thereof.

The process described above is repeated as many times as necessary and a distribution of E_mu is created. The sample is then attributed a score that corresponds to a percentile of this distribution.

For statistical analysis using a permutation test, the dataset is sorted firstly into two groups, the test-loci and the reference loci. For each group, subsets of groups are created, where loci are categorized according to their GC-content similarity (see columns 2 and 3 of the non-limiting sample Table 2 below). The number of loci present in each test subgroup is also recorded. The loci of the test group are utilized to calculate an estimate of the test-group's read-depth, say Yobs. A representative number from each GC-content subgroup may be selected to do so. Any number of methods may be used to provide a read-depth estimate, such as but not limited to, the mean, median or mode of the chosen loci.

TABLE 2 Reference test loci read- Test loci loci GC depth read-depth num Merging of loci 40% x₁ ⁴⁰, x₂ ⁴⁰, . . . , x_(nx40) ⁴⁰ y₁ ⁴⁰, y₂ ⁴⁰, . . . , y_(ny40) ⁴⁰ ny40 x₁ ⁴⁰, . . . , x_(nx40) ⁴⁰, y₁ ⁴⁰, . . . , y_(ny40) ⁴⁰ 41% x₁ ⁴¹, x₂ ⁴¹, . . . , x_(nx41) ⁴¹ y₁ ⁴¹, y₂ ⁴¹, . . . , y_(ny41) ⁴¹ ny41 x₁ ⁴¹, . . . , x_(nx41) ⁴¹, y₁ ⁴¹, . . . , y_(ny41) ⁴¹ 42% x₁ ⁴², x₂ ⁴², . . . , x_(nx42) ⁴² y₁ ⁴², y₂ ⁴², . . . , y_(ny42) ⁴² ny42 x₁ ⁴², . . . , x_(nx42) ⁴², y₁ ⁴², . . . , y_(ny42) ⁴² . . . . . . . . . . . . . . . A distribution to test Yobs is then built utilizing loci irrespective of their test or reference status as follows. The test and reference loci of each GC-content subgroup (see last column of sample Table 2) are combined to allow for calculation of a new read-depth estimate. From each merged subgroup a number of loci are chosen at random, where this number is upper-bounded by the number of test-loci utilized in the original calculation of Yobs (e.g., for GC content 40%, and in the context of the non-limiting sample Table 2, this number of loci may be in the range [1,ny40]). The new read-depth estimate is calculated from all the chosen loci. The procedure is iterated as many times as necessary in order to build a distribution of observed means. A sample is then attributed a score that corresponds to the position of Yobs in this distribution using a suitable transformation that accounts for the moments of the built distribution. As with the already described methods, it is appreciated that slight variation in % GC content is allowed (e.g., 1%-2%), if deemed appropriate. Hence, reference to a specific GC-content could be taken as “about” or “approximate”, so that for example when referring to a 40% GC-content, loci that are “approximately” or “about” 40% (e.g., 39%-41%) may be utilized in the method.

For statistical analysis using a binomial test of proportions, fragment-sizes aligned to TACS-specific genomic coordinates are used. It has been shown that fragments of cell free genetic material originating from the placenta tend to be smaller in length when compared to other cell free genetic material (Chan, K. C. (2004) Clin. Chem. 50:88-92). Hence, the statistic of interest is whether the proportion of small-size fragments aligned to a TACS-specific test-region deviates significantly from what is expected when comparing it to the respective proportion of other TACS-specific reference-regions, as this would indicate fetal genetic abnormalities.

Thus, fragment-sizes are assigned into two groups. Sizes related to the test loci are assigned to one group and fragment-sizes related to the reference loci are assigned to the other group. Subsequently, in each group, fragment sizes are distributed into two subgroups, whereby small-size fragments are assigned into one subgroup and all remaining fragments are designated to the remaining subgroup. The last step computes the proportion of small-sized fragments in each group and uses these quantities in a binomial test of proportions. The score of the test is attributed to the sample under investigation.

The final result of a sample may be given by combining one or more scores derived from the different statistical methods, non-limiting examples of which are given in Example 4.

Kits of the Invention

In another aspect, the invention provides kits for carrying out the methods of the disclosure. In one embodiment, the kit comprises a container consisting of the pool of TACS and instructions for performing the method. In one embodiment, the TACS are provided in a form that allows them to be bound to a solid support, such as biotinylated TACS. In another embodiment, the TACS are provided together with a solid support, such as biotinylated TACS provided together with streptavidin-coated magnetic beads.

In one embodiment, the kit comprises a container comprising the pool of TACS and instructions for performing the method, wherein the pool of TACS comprises a plurality of member sequences, wherein:

-   -   (i) each member sequence within the TACS pool is between 100-500         base pairs in length, each member sequence having a 5′ end and a         3′ end;     -   (ii) each member sequence binds to the same genomic sequence of         interest at least 50 base pairs away, on both the 5′ end and the         3′ end, from regions harboring Copy Number Variations (CNVs),         Segmental duplications or repetitive DNA elements; and     -   (iii) the GC content of the pool of TACS is between 19% and 80%,         as determined by calculating the GC content of each member         within the pool of TACS.

In one embodiment, the pool of TACS comprises a plurality of TACS families, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest,

Furthermore, any of the various features described herein with respect to the design and structure of the TACS can be incorporated into the TACS that are included in the kit.

In various other embodiments, the kit can comprise additional components for carrying out other aspects of the method. For example, in addition to the pool of TACS, the kit can comprise one or more of the following (i) one or more components for isolating cell free DNA from a biological sample (e.g., as described in Example 1); (ii) one or more components for preparing the sequencing library (e.g., primers, adapters, buffers, linkers, restriction enzymes, ligation enzymes, polymerase enzymes and the like as described in detail in Example 1); (iii) one or more components for amplifying and/or sequencing the enriched library (e.g., as described in Example 3); and/or (iv) software for performing statistical analysis (e.g., as described in Example 4).

Fragment-Based Analysis

In another aspect, the invention pertains to fragment based analysis of samples, described further in Example 7. There is evidence from the literature that some genetic abnormalities, such as specific types of cancer, can be characterized by and/or associated with fragments in the plasma having a smaller size than the expected size of fragments originating from healthy tissues (Jiang et al, (2015), Proceedings of the National Academy of Sciences, 112(11), ppE1317-E1325). The same hypothesis holds true for fragments originating from the placenta/fetus. Specifically, placenta derived fragments are generally of smaller size when compared to fragments originating from maternal tissues/cells (Chan, K. C. (2004) Clin. Chem. 50:88-92). Accordingly, a fragment size-based test was developed and assessed, demonstrating its ability to identify samples harboring chromosomal abnormalities.

Thus, the fragments-based detection may be used to detect abnormalities in mixed samples with low signal-to-noise ratio. For example, a binomial test of proportions, as described Example 4, can be used for the detection of increased presence of nucleic acid material originating from fetal cells based on fragment size. In particular, under the null hypothesis that the distribution of fragment sizes originating from both fetal and maternal cells is the same, a binomial test for proportions (as described in Example 4) using continuity correction can be utilized to quantify any evidence against it. Furthermore, since the size of fragments is associated with the origin of cell free DNA from specific tissues (Chan, K. C. (2004) Clin. Chem. 50:88-92) they can also be leveraged for the detection of small-sized mutations, such as point mutations and mutational signatures. In particular, for purposes of detection only certain sizes of fragments may be utilized towards the analysis and as such increasing the signal-to-noise ratio of the data across the various classification/detection methods.

EXAMPLES

The present invention is further illustrated by the following examples, which should not be construed as further limiting. The contents of all references, appendices, Genbank entries, patents and published patent applications cited throughout this application are expressly incorporated herein by reference in their entirety.

Example 1: Maternal Sample Collection and Library Preparation

The general methodology for the TACS-based multiplexed parallel analysis approach for genetic assessment is shown schematically in FIG. 1. In this example, methods for collecting and processing a maternal plasma sample (containing maternal and fetal DNA), followed by sequencing library preparation for use in the methodology of FIG. 1 are described.

Sample Collection

Plasma samples were obtained anonymously from pregnant women after the 10^(th) week of gestation. Protocols used for collecting samples for our study were approved by the Cyprus National Bioethics Committee, and informed consent was obtained from all participants.

Sample Extraction

Cell Free DNA was extracted from 2-4 ml plasma from each individual using a manual or automated extraction method suitable for cell free DNA isolation such as for example, but not limited to, Qiasymphony protocol suitable for cell free fetal DNA isolation (Qiagen) (Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855).

Sequencing Library Preparation

Extracted DNA from maternal plasma samples was used for sequencing library construction. Standard library preparation methods were used with the following modifications. A negative control extraction library was prepared separately to monitor any contamination introduced during the experiment. During this step, 5′ and 3′ overhangs were filled-in, by adding 12 units of T4 polymerase (NEB) while 5′ phosphates were attached using 40 units of T4 polynucleotide kinase (NEB) in a 100 μl reaction and subsequent incubation at 25° C. for 15 minutes and then 12° C. for 15 minutes. Reaction products were purified using the MinElute kit (Qiagen). Subsequently, adaptors P5 and P7 (see adaptor preparation) were ligated at 1:10 dilution to both ends of the DNA using 5 units of T4 DNA ligase (NEB) in a 40 μl reaction for 20 minutes at room temperature, followed by purification using the MinElute kit (Qiagen). Nicks were removed in a fill-in reaction with 16 units of Bst polymerase (NEB) in a 40 μl reaction with subsequent incubation at 65° C. for 25 minutes and then 12° C. for 20 minutes. Products were purified using the MinElute kit (Qiagen). Library amplification was performed using a Fusion polymerase (Herculase II Fusion DNA polymerase (Agilent Technologies) or Pfusion High Fidelity Polymerase (NEB)) in 50 μl reactions and with the following cycling conditions, 95° C. for 3 minutes; followed by 10 cycles at 95° C. for 30 seconds, 60° C. for 30 seconds, 72° C. for 30 seconds and finally 72° C. for 3 minutes (Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855). The final library products were purified using the MinElute Purification Kit (Qiagen) and measured by spectrophotometry.

Adaptor Preparation

Hybridization mixtures for adapter P5 and P7 were prepared separately and incubated for 10 seconds at 95° C. followed by a ramp from 95° C. to 12° C. at a rate of 0.1° C./second. P5 and P7 reactions were combined to obtain a ready-to-use adapter mix (100 μM of each adapter). Hybridization mixtures were prepared as follows: P5 reaction mixture contained adaptor P5_F (500 μM) at a final concentration of 200 μM, adaptor P5+P7_R (500 μM) at a final concentration of 200 μM with 1× oligo hybridization buffer. In addition, P7 reaction mixture contained adaptor P7_F (500 μM) at a final concentration of 200 μM, adapter P5+P7_R(500 μM) at a final concentration of 200 μM with 1× oligo hybridization buffer (Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855). Sequences were as follows, wherein *=a phosphorothioate bond (PTO) (Integrated DNA Technologies):

adaptor P5_F: (SEQ ID NO: XX) A*C*A*C*TCTTTCCCTACACGACGCTCTTCCG*A*T*C*T adaptor P7_F: (SEQ ID NO: YY) G*T*G*A*CTGGAGTTCAGACGTGTGCTCTTCCG*A*T*C*T, adaptor_P5+P7_R: (SEQ ID NO: ZZ) A*G*A*T*CGGAA*G*A*G*C.

Example 2: TArget Capture Sequences (TACS) Design and Preparation

This example describes preparation of custom TACS for the detection of whole or partial chromosomal abnormalities for chromosomes 13, 18, 21, X, Y or any other chromosome, as well as other genetic abnormalities, such as but not limited to, microdeletion/microduplication syndromes, translocations, inversions, insertions, and other point or small size mutations. The genomic target-loci used for TACS design were selected based on their GC content and their distance from repetitive elements (minimum 50 bp away). TACS size can be variable. In one embodiment of the method the TACS range from 100-500 bp in size and are generated through a PCR-based approach as described below. The TACS were prepared by simplex polymerase chain reaction using standard Taq polymerase, primers designed to amplify the target-loci, and normal DNA used as template. The chromosomal regions used to design primers to amplify suitable loci on chromosomes 13, 18, 21 and X, to thereby prepare the pool of TACS for analysis of chromosomes 13, 18, 21 and X, are shown in FIG. 2.

All custom TACS were generated using the following cycling conditions: 95° C. for 3 minutes; 40 cycles at 95° C. for 15 seconds, 60° C. for 15 seconds, 72° C. for 12 seconds; and 72° C. for 12 seconds, followed by verification via agarose gel electrophoresis and purification using standard PCR clean up kits such as the Qiaquick PCR Purification Kit (Qiagen) or the NucleoSpin 96 PCR clean-up (Mackerey Nagel) or the Agencourt AMPure XP for PCR Purification (Beckman Coulter). Concentration was measured by Nanodrop (Thermo Scientific).

Example 3: TACS Hybridization and Amplification

This example describes the steps schematically illustrated in FIG. 1 of target capture by hybridization using TACS, followed by quantitation of captured sequences by Next Generation Sequencing (NGS).

TACS Biotinylation

TACS were prepared for hybridization, as previously described (Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855), starting with blunt ending with the Quick Blunting Kit (NEB) and incubation at room temperature for 30 minutes. Reaction products were subsequently purified using the MinElute kit (Qiagen) and were ligated with a biotin adaptor using the Quick Ligation Kit (NEB) in a 40 μl reaction at RT for 15 minutes. The reaction products were purified with the Min Elute kit (Qiagen) and were denatured into single stranded DNA prior to immobilization on streptavidin coated magnetic beads (Invitrogen).

TACS Hybridization

Amplified libraries were mixed with blocking oligos (Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855) (200 μM), 5 μg of Cot-1 DNA (Invitrogen), 50 μg of Salmon Sperm DNA (Invitrogen), Agilent hybridization buffer 2×, Agilent blocking agent 10×, and were heated at 95° C. for 3 minutes to denature the DNA strands. Denaturation was followed by 30 minute incubation at 37° C. to block repetitive elements and adaptor sequences. The resulting mixture was then added to the biotinylated TACS. All samples were incubated in a rotating incubator for 12-48 hours at 66° C. After incubation, the beads were washed as described previously and DNA was eluted by heating (Koumbaris, G. et al. (2016) Clinical chemistry, 62(6), pp. 848-855). Eluted products were amplified using outer-bound adaptor primers. Enriched amplified products were pooled equimolarly and sequenced on a suitable platform.

If appropriate, amplification may be biased toward amplification of specific/desired sequences. In one embodiment of the method, this is performed when amplification is performed in the presence of sequences that hybridize to the undesired sequence of interest, and as such block the action of the polymerase enzyme during the process. Hence, the action of the amplification enzyme is directed toward the sequence of interest during the process.

Example 4: Bioinformatics Sample Analysis

This example describes representative statistical analysis approaches for use in the methodology illustrated in FIG. 1 (“analysis pipeline” in FIG. 1).

Human Genome Alignment

For each sample, the bioinformatic pipeline routine described below was applied in order to align the sample's sequenced DNA fragments to the human reference genome. Targeted paired-end read fragments obtained from NGS results were processed to remove adaptor sequences and poor quality reads (Q-score<25) using the cutadapt software (Martin, M. et al. (2011) EMB.netJournal 17.1). The quality of the raw and/or processed reads as well as any descriptive statistics which aid in the assessment of quality check of the sample's sequencing output were obtained using the FastQC software (Babraham Institute (2015) FastQC) and/or other custom-built software. Processed reads which were at least 25 bases long were aligned to the human reference genome built hg19 (UCSC Genome Bioinformatics) using the Burrows-Wheel Alignment algorithm (Li, H. and Durbin, R. (2009) Bioinformatics 25:1754-1760) but other algorithms known to those skilled in the art may be used as well. If relevant, duplicate reads were removed post-alignment. Where applicable, sequencing output pertaining to the same sample but processed on separate sequencing lanes, was merged to a single sequencing output file. The removal of duplicates and merging procedures were performed using the Picard tools software suite (Broad Institute (2015) Picard) and/or the Sambamba tools software suite (Tarasov, Artem, et al. “Sambamba: fast processing of NGS alignment formats.” Bioinformatics 31.12 (2015): 2032-2034.). A realignment procedure, using tools known to those in the art, may also be performed.

The above software analysis resulted in a final aligned version of a sequenced sample against the human reference genome and all subsequent steps were based on this aligned version. Information in terms of Short Nucleotide Polymorphisms (SNPs) at loci of interest was obtained using bcftools from the SAMtools software suite (Li, H. et al. (2009) Bioinformatics 25:2078-2079) and/or other software known to those skilled in the art. The read-depth per base, at loci of interest, was obtained using the mpileup option of the SAMtools software suite, from here on referred to as the mpileup file. Information pertaining to the size of the aligned fragments was obtained using the view option of the SAMtools software suite, from here on referred to as the fragment-sizes file and/or other software known to those skilled in the art.

The mpileup file and the fragment-sizes file were processed using custom-build application programming interfaces (APIs) written in the Python and R programming languages (Python Software Foundation (2015) Python; The R Foundation (2015) The R Project for Statistical Computing). The APIs were used to determine the ploidy state of chromosomes of interest, and/or other genetic abnormalities in regions of interest across the human genome, using a series of steps (collectively henceforth referred to as the “algorithm”) and to also collect further descriptive statistics to be used as quality check metrics, such as but not limited to fetal fraction quantification (collectively henceforth referred to as the “QC metrics”). The APIs can also be used for the assessment of genetic abnormalities from data generated when applying the described method in cases of multiple gestation pregnancies, as well as other genetic abnormalities such as, but not limited to, microdeletions, microduplications, copy number variations, translocations, inversions, insertions, point mutations and mutational signatures.

QC Metrics

QC metrics were used to inspect an aligned sample's properties and decide whether the sample was suitable to undergo classification. These metrics were, but are not limited to:

(a) The enrichment of a sample. The patterns of enrichment are indicative of whether a sample has had adequate enrichment across loci of interest in a particular sequencing experiment (herein referred to as a “run”). To assess this, various metrics are assessed, non-limiting examples of which are:

-   -   (i) overall sample on-target read depth,     -   (ii) sample on-target sequencing output with respect to total         mapped reads,     -   (iii) individual TACS performance in terms of achieved         read-depth,     -   (iv) kurtosis and skewness of individual TACS enrichment,     -   (v) kurtosis and skewness moments that arise from all TACS,     -   (vi) fragment size distribution,     -   (vii) percentage of duplication     -   (viii) percentage of paired reads and,     -   (ix) percentage of aligned reads,         if applicable.         The above checks are also taken into consideration with regards         to GC-bias enrichment. Samples that fail to meet one or more of         the criteria given above are flagged for further inspection,         prior to classification.

(b) A sample's fetal fraction or fraction of interest. Samples with an estimated fetal fraction, or fraction of interest, that is below a specific threshold are not classified. Furthermore, if applicable the fraction of interest may be calculated using more than one method and concordance of results between estimation methods may be used as an additional QC prior to classification.

The Algorithm

The algorithm is a collection of data processing, mathematical and statistical model routines arranged as a series of steps. The algorithm's steps aim in deciding the relative ploidy state of a chromosome of interest with respect to all other chromosomes of the sequenced sample and is used for the detection of whole or partial chromosomal abnormalities for chromosomes 13, 18, 21, X, Y or any other chromosome, as well as other genetic abnormalities such as, but not limited to, microdeletion/microduplication syndromes and other point or small size mutations. As such the algorithm can be used, but is not limited to, the detection of whole or partial chromosomal abnormalities for chromosomes 13, 18, 21, X,Y or any other chromosome, as well as other genetic abnormalities such as, but not limited to, microdeletions, microduplications, copy number variations, translocations, inversions, insertions, point mutations and other mutational signatures. The algorithm carries out, but is not limited to, two types of assessments, one pertaining to the read-depth information of each sample and the other to the distribution of fragment-sizes, across TACS-specific regions. One or more statistical tests may be associated with each type of assessment, non-limiting examples of which are given in the statistical methods described herein.

In the case of read-depth associated tests, the algorithm compares sequentially the read-depth of loci from each chromosome of interest (herein referred to as the test chromosome) against the read-depth of all other loci (herein referred to as the reference loci) to classify its ploidy state. For each sample, these steps were, but are not limited to:

(a) Removal of inadequately sequenced loci. The read-depth of each locus was retrieved. Loci that have not achieved a minimum number of reads, were considered as inadequately enriched and were removed prior to subsequent steps.

(b) Genetic (GC-content) bias alleviation. The sequencing procedure may introduce discrepancies in read-depth across the loci of interest depending on their GC content. To account for such bias, a novel sequence-matching approach that increases both sensitivity and specificity to detect chromosomal aneuploidies was employed. The GC content of each locus on the test chromosome was identified and similar genetic loci were grouped together to form genetically matched groups. The procedure was repeated for the reference loci. Then, genetically matched groups from the test chromosome were conditionally paired with their genetically matched group counterparts on the reference chromosome(s). The groups may have any number of members. The conditionally matched groups were then used to assess the ploidy status of test chromosomes.

(c) Genetic abnormality determination. Ploidy status determination, or other genetic abnormalities of interest such as but not limited to microdeletions, microduplications, copy number variations, translocations, inversions, insertions, point mutations and other mutational signatures was achieved using a single statistical method and/or a weighted score approach on the result from the following, but not limited to, statistical methods:

Statistical Method 1: The differences in read-depth of the conditionally paired groups were tested for statistical significance using the t-test formula:

$t = \frac{\hat{x} - \mu}{s/\sqrt{n}}$

where t is the result of the t-test, {circumflex over (x)} is the average of the differences of the conditionally paired groups, μ is the expected read-depth and is set to a value that represents insignificant read-depth differences between the two groups, s the standard deviation of the differences of the conditionally paired groups and n the length of the vector of the conditionally paired differences. The magnitude of the t-score was then used to identify evidence, if any, against the null hypothesis of same ploidy between reference and test chromosomes. Specifically, t>=c1 (where C1 is a predefined threshold belonging to the set of all positive numbers) shows evidence against the null hypothesis of no difference.

Statistical Method 2: Bivariate nonparametric bootstrap. The bootstrap method depends on the relationship between the random variables X (read-depth of reference loci) and Y (read-depth of test loci). Here, the read depth of baits on the reference group (random variable denoted by X) were treated as the independent covariate. The first step of the iterative procedure involved random sampling with replacement (bootstrapping) of the read-depths of loci on the reference chromosomes, i.e. (x1,g1), . . . , (xn,gn), where the parameter g is known and denotes the GC-content of the chosen bait. Then, for each randomly selected reference bait (xi,gi), a corresponding read depth was generated for a genetically matched locus i.e. (y1,g1), . . . , (yn,gn). Thus, the bivariate data (x1,y1), (x2,y2), . . . , (xn,yn) was arrived at, which was conditionally matched on their GC-content (parameter gi). The differences between the read depths of the genetically matched bootstrapped values xi and yi were used to compute the statistic of interest in each iteration. In one embodiment this statistical measure can be, but is not limited to, the mode, mean or median of the recorded differences, and/or multiples thereof. The procedure was repeated as necessary to build up the distribution of the statistic of interest from these differences. The sample was assigned a score that corresponds to a specific percentile of the built distribution (e.g. 5th percentile). Under the null hypothesis the ploidy between chromosomes in the reference and test groups is not different. As such, samples whose score for a particular chromosome, was greater than a predefined threshold, say c2, were classified as statistically unlikely to have the same ploidy. Other statistical measures may be employed.

Statistical Method 3: Stratified permutation test. The statistic of interest is the read-depth estimate of the test chromosome, denoted by, Ŷ_(obs) which is calculated using all loci of the test chromosome's genetically matched groups as follows:

${\hat{Y}}_{obs} = \frac{\sum_{j = 1}^{j = T}{\sum_{i = 1}^{i = {Nj}}y_{ij}}}{\sum_{j = 1}^{j = T}{Nj}}$

where y_(ij) is the read-depth of locus i part of the genetically matched group j (i.e., loci belonging to a specific group based on their GC-content), Nj is the number of test loci part of the genetically matched group j and T the number of genetically matched groups. Subsequently, a null distribution to test Ŷ_(obs) was built. To do so, for each group j, the test and reference loci were combined (exchangeability under the null hypothesis), and each group j was sampled randomly up to Nj times without replacement (stratified permutation). This created a vector of values, say yi, and from this the vector's average value, say, was calculated. The procedure was repeated as necessary to build the null distribution. Finally, Ŷ_(obs) was studentised against the null distribution using the formula:

$Z_{Yobs} = \frac{Y_{obs}^{\bigwedge} - \hat{Y}}{\sigma_{Y}}$

where Ŷ and σ_(Y) are the first and square root of the second moment of all permuted ý_(i) statistic values. Samples whose Z_(Yobs) was greater than a predefined threshold, say c3, were statistically less likely to have the same ploidy in the reference and test groups.

In the case of fragment-size associated tests, the algorithm computes the proportion of small-size fragments found in test-loci and compares it with the respective proportion in reference-loci as described in Statistical Method 4 below.

Statistical Method 4: Fragment Size Proportions. For each sample the number and size of fragments aligned onto the human reference genome at the corresponding TACS coordinates, is extracted. The data is subsequently filtered so as to remove fragment-sizes considered statistical outliers using the median outlier detection method. Specifically, outliers are defined as those fragments whose size is above or below the thresholds, F_(thr), set by equation:

F _(thr) =F _(median)±(X×IQR)

where F_(median) is the median fragment-size of all fragments of a sample, X is a variable that can take values from the set of R+, and IQR is the interquartile range of fragment sizes. Thereafter, a binomial test of proportions is carried out to test for supporting evidence against the null hypothesis, H0, where this is defined as: H0: The proportion of small fragments of the test-region is not different from the proportion of small-fragments of the reference region. In various embodiments of the invention, small fragments are defined as those fragments whose size is less than or equal to a subset of Z+that is upper-bounded by 160 bp. If the set of all TACS are defined as T, then the test region can be any proper subset S which defines the region under investigation, and the reference region is the relative complement of S in T. For example, in one embodiment of the invention, the set S is defined by all TACS-captured sequences of chromosome 21 and thus the reference set is defined by all TACS-captured fragments on the reference chromosomes, and/or other reference loci The alternative hypothesis, H1, is defined as: H1: The proportion of small fragments of the test-region is not equal to the proportion of test fragments of the reference region. As such, and taking into account continuity correction, the following score is computed (Brown et. al, Harrel):

$W_{test} = {\left( {p^{\prime} - p_{ref}} \right)/\sqrt{\frac{p^{\prime}\left( {1 - p^{\prime}} \right)}{N_{test}}}}$ where $p^{\prime} = \frac{\left( {F^{\prime} + 0.5} \right)}{\left( {N_{test} + 1} \right)}$ $p_{ref} = \frac{\left( {F_{ref} + 0.5} \right)}{\left( {N_{ref} + 1} \right)}$

{acute over (F)} is the number of small-size fragments on the test-region, F_(ref) the number of small size fragments on the reference region, N_(test) the number of all fragments on the test region and N_(ref) the number of all fragments on the reference region.

For each sample, the algorithm tests sequentially the proportion of fragment sizes of regions under investigation (for example, but not limited to, chromosome 21, chromosome 18, chromosome 13 or other (sub)chromosomal regions of interest) against reference regions; those not under investigation at the time of testing. For each sample a score is assigned for each test. Scores above a set-threshold, say c4, provide evidence against the null hypothesis.

Weighted Score method 1: In one embodiment of the method, a weighted score was attributed to each sample s, computed as a weighted sum of all statistical methods using the formula:

V _(s)(R,F)=z ₁ max{R _(s) ,F _(s)}+(1−z ₁)min{R _(s) ,F _(s)}

where R_(s) is the run-specific corrected score arising from a weighted contribution of each read-depth related statistical method for sample s and is defined as:

$R_{s} = \frac{\left( {{\sum_{i}{w_{i}S_{is}}} - R_{r}^{\prime}} \right)}{\sigma_{r}}$

and Ŕ_(r) is the run-specific median value calculated from the vector of all unadjusted read-depth related weighted scores that arise from a single sequencing run, and σ_(r) is a multiple of the standard deviation of R scores calculated from a reference set of 100 euploid samples. The terms max{R_(s),F_(s)} and min{R_(s),F_(s)} denote the maximum and minimum values of the bracketed set, respectively. F_(s) is the run-specific corrected score arising from the fragment-size related statistical method and is defined as:

$F_{s} = \frac{\left( {W_{test} - R_{f}^{\prime}} \right)}{\sigma_{f}}$

where W_(test) is as defined earlier, Ŕ_(f) is the run specific median calculated from the vector of all unadjusted fragment-related statistical scores that arise from a single sequencing run, and σ_(f) is a multiple of the standard deviation of F scores calculated from a reference set of 100 euploid samples.

A unique classification score of less than a predefined value indicates that there is no evidence from the observed data that a sample has a significant risk of aneuploidy.

Weighted Score method 2: In another embodiment of the method, the weighted score arising from the statistical methods described above was used to assign each sample a unique genetic abnormality risk score using the formula:

${R\left( {t,c} \right)} = {\sum\limits_{j = 0}^{j = N}{w_{j}\frac{t_{j}}{c_{j}}}}$

where R is the weighted score result, w_(j) the weight assigned to method j, t_(j) the observed score resulting from method j, and c_(j) the threshold of method j.

A unique classification score of less than a predefined value indicates that there is no evidence from the observed data that a sample has a significant risk of aneuploidy.

Since all read depths from baits in the reference group were assumed to be generated from the same population, and in order to have a universal threshold, run-specific adjustments were also employed to alleviate run-specific biases.

The aforementioned method(s), are also suitable for the detection of other genetic abnormalities, such as but not limited to, subchromosomal abnormalities. A non-limiting example is the contiguous partial loss of chromosomal material leading to a state of microdeletion, or the contiguous partial gain of chromosomal material leading to a state of microduplication. A known genetic locus subject to both such abnormalities is 7q11.23. In one embodiment of statistical method 1, synthetic plasma samples of 5%, 10% and 20% fetal material were tested for increased risk of microdeletion and/or microduplication states for the genetic locus 7q11.23.

For point mutations various binomial tests are carried out that take into consideration the fetal fraction estimate of the sample, f, the read-depth of the minor allele, r, and the total read-depth of the sequenced base, n. Two frequent, yet non-limiting examples involve assessment of the risk when the genetic abnormality is a recessive point mutation or a dominant point mutation.

In the non-limiting example of a recessive point mutation the null hypothesis tested is that both the mother and the fetus are heterozygous (minor allele frequency is 0.5) against the alternative in which the fetus is homozygous (minor allele frequency is 0.5−f/2). A small p-value from the corresponding likelihood ratio test would indicate evidence against the null. In the non-limiting example of a dominant point mutation the null hypothesis tested is that the mother and fetus are homozygous at the given position against the alternative in which only the fetus is heterozygous for the given position. A small p-value from the corresponding likelihood ratio test would indicate evidence against the null.

In addition to the above, fetal sex determination methods were also developed, with non-limiting examples given below. In one embodiment of the invention, fetal sex was assigned to a sample using a Poisson test using the formula:

${\Pr \left( {r_{y} \leq k} \right)} = {e^{- \lambda}{\sum\limits_{i = 0}^{i = k}\frac{\lambda^{i}}{i!}}}$ where: $\lambda = \frac{{fB}\; \mu}{2}$

and f is the fetal fraction estimate of the sample, B is the number of target sequences on chromosome Y, μ is the read-depth of the sample and k is the sum of reads obtained from all targets B. The null hypothesis of the Poisson test was that the sample is male. A value of Pr(r_(y)) less than a threshold c_(y) was considered as enough evidence to reject the null hypothesis, i.e. the sample is not male. If any of the terms for computing Pr(r_(y)) were unavailable, then the sample's sex was classified as NA (not available).

In another embodiment of the invention, fetal sex was assigned using the average read-depth of target sequences on chromosome Y. If the average read-depth of the target-sequences was over a predefined threshold, where such threshold may be defined using other sample-specific characteristics such as read-depth and fetal-fraction estimate, the fetal sex was classified as male. If the average read-depth was below such threshold then the sample was classified as female.

Fetal Fraction Estimation/Fraction of Interest Estimation

Several methods have been developed to estimate fetal fraction that can be applied to singleton and/or to multiple gestation pregnancies. As such, and dependent on the type of pregnancy, the fetal fraction estimate can be obtained from either method or as a weighted estimate from a subset and/or all developed methods. Some non-limiting examples are given below.

In one embodiment, a machine learning technique has been developed based on Bayesian inference to compute the posterior distribution of fetal DNA fraction using allelic counts at heterozygous loci in maternal plasma of singleton pregnancies. Three possible informative combinations of maternal/fetal genotypes were utilized within the model to identify those fetal DNA fraction values that get most of the support from the observed data.

Let f denote the fetal DNA fraction. If the mother is heterozygous at a given genomic locus, the fetal genotype can be either heterozygous or homozygous resulting in expected minor allele frequencies at 0.5 and 0.5−f/2, respectively. If the mother is homozygous and the fetus is heterozygous then the expected minor allele frequency will be f/2. A Markov chain Monte Carlo method (a Metropolis-Hastings algorithm) (The R Foundation (2015) The R Project for Statistical Computing) was used with either a non-informative or an informative prior (i.e. incorporate additional information such as gestational age, maternal weight etc.) to obtain a sequence of random samples from the posterior probability distribution of fetal DNA fraction that is based on a finite mixture model.

In another embodiment, the fetal fraction estimate is computed only from the fetus-specific minor allele frequency (MAF) cluster, i.e., the cluster formed when the mother is homozygous and the fetus is heterozygous for a given genomic locus. It is assumed that the mean value of the fetal fraction estimate is normally distributed as N(2{acute over (x)},σ_({acute over (x)})), where {acute over (x)} is the mean of the fetus-specific MAF, and σ_({acute over (x)}) is the standard deviation of the fetus-specific MAF. The fetal fraction estimate is then obtained from percentiles of the computed distribution, N(2{acute over (x)},σ_({acute over (x)})).

For multiple gestation pregnancies, non-limiting examples of which include monozygotic and dizygotic twin pregnancies, triplet pregnancies and various egg and/or sperm donor cases, the fetal fraction can be estimated using information obtained from heterozygous genetic loci whose MAF value is less than a threshold, say M_(thresh), and derived from potential fetus-specific SNPs. The ordinarily skilled artisan will appreciate that fetus specific SNPs can originate from any fetus, or from any possible combination of the fetuses or from all the fetuses of the gestation. As such, an algorithm that estimates the fetal fraction of the fetus with the smallest contribution to the total fetal content, by taking into account the combinatorial contribution of each fetus to the MAF values that define fetus-specific SNPs, and also allows for inhomogeneous contribution of fetal material to the total fetal content of plasma derived material has been developed. To this effect, a two-step approach is employed by the algorithm.

In one embodiment of the algorithm, the multiple gestation pregnancy under consideration is a dizygotic twin pregnancy. As a first step, the algorithmic implementation of the model utilizes all informative SNPs and allows for inhomogeneous fetal contribution that can be explained with a fold-difference in fetal fraction estimates of a set threshold, say cf. Specifically, if f1 and f2 represent the fetal fractions of fetus one and fetus two, and f1<=f2, then the assumption is that f2<=cf f1, with cf being a positive real number greater than or equal to 1. Under this assumption, the observed data D, defined as counts of the alternate and reference alleles at informative SNP loci, are believed to be generated from a mixture distribution of three Binomials (defined by parameters, f1/2, f2/2 and (f1+f2)/2), with the posterior distribution p(f1,f2|D) being proportional to the observational model which can be written as p(f1|f2,D) p(f2|D). The posterior distribution p(f1,f2|D) is sampled with an MCMC Metropolis-Hastings algorithm using a uniform prior. The empirical quantile approach is performed on the generated data array to infer the fetal fractions.

As a second step, the algorithm runs a model-based clustering algorithm (Finite Gaussian mixture modeling fitted via EM algorithm; R-package: mclust) to identify whether there exists a separate outlier SNP cluster which is believed to be centered around f1/2. Existence of such a cluster with a mean invalidating the cf>=f2/f1 assumption, leads to estimation of f1 using only SNPs part of the identified cluster.

The methods described above are suited to the determination of the fraction of any component of interest part of a mixed sample. As such, the methods are not to be understood as applicable only to the application of fetal fraction estimation and can be applied to the estimation of any component of interest part of a mixed sample.

Example 5: Target Enrichment Using Families of TACS

In this example, a family of TACS, containing a plurality of members that all bind to the same target sequence of interest, was used for enrichment, compared to use of a single TACS binding to a target sequence of interest. Each member of the family of TACS bound to the same target sequence of interest but had a different start and/or stop coordinates with respect to a reference coordinate system for that target sequence (e.g., the human reference genome built hg19). Thus, when aligned to the target sequence, the family of TACS exhibit a staggered binding pattern, as illustrated in FIG. 3. Typically, the members of a TACS family were staggered approximately 5-10 base pairs.

A family of TACS containing four members (i.e., four sequences that bound to the same target sequence but having different start/stop positions such that the binding of the members to the target sequence was staggered) was prepared. Single TACS hybridization was also prepared as a control. The TACS were fixed to a solid support by labelling with biotin and binding to magnetic beads coated with a biotin-binding substance (e.g., streptavidin or avidin) as described in Example 3. The family of TACS and single TACS were then hybridized to a sequence library, bound sequences were eluted and amplified, and these enriched amplified products were then pooled equimolarly and sequenced on a suitable sequencing platform, as described in Example 3.

The enriched sequences from the family of TACS sample and the single TACS sample were analyzed for read-depth. The results are shown in FIGS. 4A and 4B. As shown in FIG. 4A, target sequences of interest enriched using the family of four TACS (red dots) exhibited a fold-change in read-depth when compared to control sequences that were subjected to enrichment using only a single TACS (blue dots). Fold-change was assessed by normalizing the read-depth of each locus by the average read-depth of a sample, wherein the average read-depth was calculated from all loci enriched with a single TACS. As shown in FIG. 4B, an overall 54.7% average increase in read-depth was observed using the family of four TACS.

This example demonstrates that use of a family of TACS, as compared to a single TACS, results in significantly improved enrichment of a target sequence of interest resulting in significantly improved read-depth of that sequence.

Example 6: Quantification of Variant Alleles in Mixed Samples Containing

Maternal DNA at Loci Associated with Genetic Conditions

Mixed samples, containing both maternal and fetal DNA, were processed as described in Example 1. Families of TACS were designed for the detection of inheritable genetic conditions associated with 5 different genetic abnormalities (β-thalassemia, phenylketonuria, cystic fibrosis, Gauchers' disease and autosomal recessive polycystic kidney disease). The members of the TACS families were designed such that they had staggered start and/or stop positions for binding to the target sequence of interest, as described in Example 5. Furthermore, the members of the TACS families were designed to have the optimized features with respect to their size, distance from repetitive elements and GC content, as described in Example 2.

The TACS methodology illustrated in FIG. 1 (and described in Examples 1-3) was used with the families of TACS for enhanced enrichment of target sequences of interest containing specific sequences relevant to the determination of maternal carrier status for five inheritable genetic conditions (β-thalassemia, phenylketonuria, cystic fibrosis, Gauchers' disease and autosomal recessive polycystic kidney disease). To determine the maternal carrier status for these genetic conditions, analysis was conducted across 14 different genes, covering a total of 157 loci. Optionally, the maternal sample can be simultaneously interrogated with TACS (or families of TACS) for detecting fetal chromosomal abnormalities (e.g., aneuploidies, such as for chromosomes 13, 18, 21, X and Y, as described herein).

Targeted sequencing products obtained from Next Generation Sequencing (NGS) results were processed to remove adaptor sequences and poor quality reads. Reads whose length was at least 25 bases long post adaptor-removal were aligned to the human reference genome built hg19. If relevant, duplicate reads are removed post-alignment. Where applicable, sequencing output pertaining to the same sample but processed on separate sequencing lanes was merged to a single sequencing output file. Software analysis provided a final aligned version of a sequenced sample against the human reference genome from which information can then be extracted in terms of Single Nucleotide Polymorphisms (SNPs), Single Nucleotide Variants (SNVs) and other genetic variations with respect to a reference sequence at loci of interest, read-depth per base and the size of aligned fragments. The maternal sample can be fully processed using the pipeline described in Examples 1-4 to determine the ploidy status of the fetus. In addition to this, information in terms of SNVs and indels at loci of interest concerning the sequence and number of times each SNV is present in a sequenced sample was detected and was used to infer the presence and carrier status the maternal sample using binomial statistics as described herein.

Data in the form of calculated Variant Allele Frequencies (VAFs) from mixed samples, containing both maternal and fetal DNA, are presented in FIG. 5. The Variant Allele Frequency was computed as the number of times the variant allele was sequenced over the number of times the locus was sequenced. The x-axis is an index of the different samples analyzed. The y-axis is the value of the Variant Allele Frequency of a sample (VAF %). The value of the VAF is based on the maternal fraction present in the mixed sample. A carrier of the variant allele would be expected to have a VAF of around 50%. However, a pregnant woman who is a carrier would be expected to have a VAF value around 50% minus half the fetal fraction value since a mixed sample contains both fetal and maternal DNA. Thus, if for example a mixed sample has an estimated fetal fraction of 10% then the maternal fraction is 90%. Thus, it is expected that maternal carrier status for autosomes (i.e. non-sex chromosomes) would have a VAF value near 45%. A similar line of reasoning may be used for sex-linked diseases where one has to take into account the sex of the fetus before estimating expected VAFs. Large value VAFs appear at the top of the plot indicating maternal carrier status (colored dots; color scheme dependent on disease status as described in the legend of FIG. 5).

For those mixed maternal/fetal samples having positive maternal carrier status, a paternal sample is then processed in order to compute the paternal carrier status and determine the fetal risk of inheriting the genetic condition. A paternal sample (e.g., plasma sample) also undergoes the TACS methodology illustrated in FIG. 1, as described herein, using families of TACS directed to those loci for which maternal sample has been determined to have positive carrier status. The sequencing data are aligned as described for the maternal sample and information in terms of Short Nucleotide Variants (SNVs) at loci of interest, read-depth per base and the size of aligned fragments is obtained. Using this information the presence and carrier status of the paternal sample is inferred using binomial statistics.

Finally, a fetal risk score for inheriting the detected genetic conditions is determined from the data using Mendelian genetics reasoning. An example of a fetal risk score is illustrated below in Table 3, where the algorithms used have detected that the mother is a carrier, with allelic sequence Aa, for a given recessive genetic condition and the father also has been determined to be a carrier, with allelic sequence Aa, for the same given recessive genetic condition.

TABLE 3 Example of Mendelian Genetics Reasoning for Determining Fetal Risk Maternal Status Possible Fetal Outcomes A a Paternal A AA Aa Status a Aa aa Accordingly, for the allelic combination of Aa, where “A” describes the dominant allele and “a” the recessive disease-associated allele and “Aa” thus implies maternal and paternal carrier of the condition, then the fetus has a 25% chance of having the genetic condition (“aa” homozygous recessive genotype in the lower right corner of Table 3 above).

In summary, this example demonstrates that the TACS methodology can successfully be used to determine maternal (and, if necessary based on the maternal results, paternal) carrier status for inheritable genetic conditions, thereby allowing for determination of fetal risk of inheriting genetic conditions.

The VAF is tested using binomial statistics. In the case that the mother is homozygous recessive, the expected value is (1−f), where f is the fetal fraction. As such, the binomial test checks if the VAF value is significantly different from (1−f). That is, the null hypothesis of the test is that the VAF value is not significantly different from (1−f). If we fail to reject the null hypothesis then it is assumed that the mother is homozygous recessive and we revert to testing the father as per the already described procedure.

Example 7: Fragment Size Based Tests

There is evidence from the literature that some types of abnormalities, such as specific types of cancer, can be characterized by and/or associated with fragments in the plasma having a smaller size than the expected size of fragments originating from healthy tissues (Jiang et al, (2015), Proceedings of the National Academy of Sciences, 112(11), ppE1317-E1325). Thus, a fragments-size based test can be utilized to detect the presence of somatic copy number variations in individuals suspected of having cancer. To this effect, a binomial test of proportions, as described Example 4, can be used for the detection of increased presence of nucleic acid material originating from non-healthy tissue (e.g., tumor tissue) based on fragment size. In particular, under the null hypothesis that the distribution of fragment sizes originating from both healthy and non-healthy cells (for example, but not limited to cancerous cells) is the same, a binomial test for proportions (as described in Example 4) using continuity correction can be utilized to quantify any evidence against it.

The same hypothesis holds true for fragments originating from the placenta/fetus. Specifically, placenta derived fragments are generally of smaller size when compared to fragments originating from maternal tissues/cells (Chan, K. C. (2004) Clin. Chem. 50:88-92). Accordingly, assessment of the fragment size-based test was performed using maternal plasma samples (i.e., mixed samples where cell free DNA is of maternal and fetal origin). The size of fragments that have aligned to TACS-enriched regions can be obtained from the aligned data. Subsequently, the proportion of fragments under a specific threshold from a test region is compared respective proportion of fragments from a reference region for evidence against the null hypothesis H0,

H0: The proportion of small fragments of the test-region is not different from the proportion of small-fragments of the reference region.

FIG. 6 shows results when applying the fragment sizes method to the mixed sample containing maternal and fetal DNA. The black dots are individual samples. The x-axis shows the sample index. The y-axis shows the score result of the fragments-based method. A score result greater than the one indicated by the threshold, illustrated as a grey line, indicates a deviation from the expected size of fragments illustrating the presence of aneuploidy. The results demonstrate that an aneuploid sample, having an estimated fetal fraction equal to 2.8%, was correctly identified, illustrating that fragments-based detection may be used to detect abnormalities in mixed samples with low signal-to-noise ratio (e.g., as is the case in detection of cancer).

Accordingly, this example demonstrates the successful ability of the fragments-based detection method in detecting genetic abnormalities in samples with low signal-to-noise ratios, thereby demonstrating the suitability of the fragments-based test for analysis of either cancer samples for oncology purposes or maternal samples for NIPT.

Since small-sized fragments are associated with fragments from non-healthy tissues (Jiang et al, (2015), Proceedings of the National Academy of Sciences, 112(11), ppE1317-E1325) they can also be leveraged for the detection of small-sized mutations, such as point mutations and mutational signatures. For example, one may only use small-sized fragments in Variant Allele Frequency estimation, thereby increasing the signal-to-noise ratio.

Example 7: Amniotic Fluid/CVS Example

Targeted sequencing products obtained from Next Generation Sequencing (NGS) results were processed to remove adaptor sequences and poor quality reads. Reads whose length was at least 25 bases long post adaptor-removal were aligned to the human reference genome built hg19. If relevant, duplicate reads are removed post-alignment. Where applicable, sequencing output pertaining to the same sample but processed on separate sequencing lanes was merged to a single sequencing output file. Local realignment of the data, using tools known in the art, may also be performed. Software analysis provided a final aligned version of a sequenced sample against the human reference genome from which information can then be extracted in terms of Single Nucleotide Polymorphisms (SNPs), Single Nucleotide Variants (SNVs) and other genetic variations with respect to a reference sequence at loci of interest, read-depth per base and the size of aligned fragments.

Each sample undergoes QC analysis, involving, but not limited to, a read-depth threshold, sample contamination from secondary DNA sources and variance threshold of average read depths of TACS. Samples that do not pass QC thresholds are flagged for further inspection. If a sample passes QC, then it proceeds to the classification stage that involves multiple levels of analysis. In one embodiment, the normalization step involves GC-bias correction using local polynomial regression to get the normalized read depth data that is used for subsequent analysis.

In one embodiment, each TACS enriched chromosome of interest is tested against the other targeted autosomes that serve as reference. In one embodiment, the test statistic is the ratio of the average read depth of the chromosome of interest over the average read depth of the reference chromosomes. For example, when we test for chromosome 21 aneuploidies a comparison of the average read depth of chr21 TACS with the average read depth of the TACS on other targeted autosomes is performed. A graphical illustration of the results of the method is shown in FIGS. 7 and 8. The example shown in FIG. 7 illustrates a normal male fetus, whilst the example of FIG. 8 illustrates the detection of aneuploidy on chromosome 18 (T18) in a male sample.

In parallel, the algorithm searches for sub-chromosomal aberrations that span our targeted regions using a Pruned Dynamic Programming segmentation algorithm (Cleynen et al. 2014) and assuming that the data is Normally distributed.

In one embodiment the algorithm is utilized to detect full chromosome aneuploidies, such as for example but not limited to chr13, 18, 21, X and Y, microdeletion syndromes and sub-chromosomal aberrations.

In another embodiment, heterozygous loci are utilised to get estimates of the ploidy. MAFs (Minor Allele frequencies at heterozygous loci) can be used to construct various Gaussian Mixtures models (one for the diploid state, and one for the aneuploid state, such as but not limited to trisomy 21, trisomy 18, trisomy 13, trisomy X, monosomy X, or double trisomies of aforementioned chromosomes). A maximum likelihood approach can be used to detect the most likely ploidy status of the sample. FIG. 10 illustrates an example of detection of trisomy 21. Each dot is a MAF value obtained from SNP data. Note the change in the MAF of the SNPs at the right hand side of the plot (chr 21 MAF values), illustrating trisomy 21. FIG. 11 illustrates a normal case (no change in MAF is seen across SNPs). FIG. 9 illustrates the application of the method in triploidy cases (all chromosomes illustrate an extra copy). Note the shift in all MAF values for all SNPs (in comparison with FIG. 5).

In another embodiment we can also utilise non-overlapping widows of a fixed size and count the number of reads that fall into these bins. If applicable, gross outlier detection and filtering is utilised before proceeding to GC-bias correction using a local polynomial method. As per the already described procedure a pruned dynamic programming segmentation method is applied on the normalized read depths to detect large aberrations genome-wide. FIG. 12 illustrates the application of this method to detect a sub-chromosomal data. 

1. A method of determining fetal risk of inheriting a genetic condition, the method comprising: (a) preparing a sequencing library from a sample comprising maternal and fetal DNA; (b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of sequences that bind to genomic sequences of interest including variant allele loci of interest associated with different genetic conditions; (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library; (d) amplifying and sequencing the enriched library; (e) performing statistical analysis on the enriched library sequences to thereby determine maternal carrier status at the loci of interest associated with different genetic conditions, wherein for a sample with a positive maternal carrier status, the method further comprises: (f) obtaining a paternal DNA sample and performing steps (a)-(e) on the paternal DNA sample to determine paternal carrier status for those diseases in which there is a positive maternal carrier status; and (g) determining fetal risk of inheriting a genetic condition based on maternal carrier status and, when (f) is performed, paternal carrier status.
 2. The method of claim 1, wherein: (i) each member sequence within the pool of TACS is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end; (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within the pool of TACS. directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest.
 4. The method of any one of claims 1 to 3, wherein the sample is a maternal plasma sample.
 5. The method of any one of claims 1 to 4, wherein the pool of TACS comprises members that bind to chromosomes 1-22, X and Y of the human genome.
 6. The method of any one of claims 1 to 5, wherein each member sequence within the pool of TACS is at least 160 base pairs in length.
 7. The method of claim 3, wherein each TACS family comprises at least 3 member sequences.
 8. The method of claim 7, wherein the pool of TACS comprises at least 5 different TACS families.
 9. The method of any one of claims 3, 7 and 8, wherein the start and/or stop positions for the member sequences within a TACS family, with respect to a reference coordinate system for the genomic sequence of interest, are staggered by at least 3 base pairs.
 10. The method of any one of claims 1 to 9, wherein the variant allele loci of interest are associated with genetic conditions selected from a group comprising Abetalipoproteinemia; Arthrogryposis Mental Retardation Seizures; Autosomal recessive polycystic kidney disease; Bardet Biedl syndrome 12; Beta thalassemia; Canavan disease; Choreacanthocytosis; Crigler Najjar syndrome, Type I; Cystic fibrosis; Factor V Leiden thrombophilia; Factor XI deficiency; Familial dysautonomia; Familial Mediterranean fever; Fanconi anemia (FANCG-related); Glycine encephalopathy (GLDC-related); Glycogen storage disease, Type 3; Glycogen storage disease, Type 7; GRACILE Syndrome; Inclusion body myopathy, Type 2; Isovaleric acidemia; Joubert syndrome, Type 2; Junctional epidermolysis bullosa, Herlitz type; Leber congenital amaurosis (LCAS-related); Leydig cell hypoplasia [Luteinizing Hormone Resistance]; Limb girdle muscular dystrophy, Type 2E; Lipoamide Dehydrogenase Deficiency [Maple syrup urine disease, Type 3]; Lipoprotein lipase deficiency; Long chain 3-hydroxyacyl-CoA dehydrogenase deficiency; Maple syrup urine disease, Type 1B; Methylmalonic acidemia (MMAA-related); Multiple sulfatase deficiency; Navajo neurohepatopathy [MPV17-related hepatocerebral mitochondrial DNA depletion syndrome]; Neuronal ceroid lipofuscinosis (MFSD8-related); Nijmegen breakage syndrome; Ornithine translocase deficiency [Hyperornithinemia-Hyperammonemia-Homocitrullinuria (HHH) Syndrome]; Peroxisome biogenesis disorders syndrome spectrum (PEX2-related); Phenylketonurea; Pontocerebellar hypoplasia, Type 2E; Pycnodysostosis; Pyruvate dehydrogenase deficiency (PDHB-related); Retinal Dystrophy (RLBP1-related) [Bothnia retinal dystrophy]; Retinitis pigmentosa (DHDDS-related); Sanfilippo syndrome, Type D [Mucopolysaccharidosis IIID]; Sickle-cell disease; Sjögren-Larsson syndrome; Tay-Sachs disease; Usher syndrome, Type 1F; 3 Methylcrotonyl CoA Carboxylase Deficiency 1; 3 Methylcrotonyl CoA Carboxylase Deficiency 2, and combinations thereof.
 11. The method of any one of claims 1 to 10, wherein the pool of TACS further comprises sequences that bind to chromosomes of interest for detecting fetal genetic abnormalities and performing statistical analysis on the enriched library sequences to thereby determine fetal risk of a genetic abnormality at the chromosome of interest.
 12. The method of claim 11, wherein the genetic abnormality is a chromosomal aneuploidy.
 13. The method of claim 12, wherein the chromosomes of interest include chromosomes 13, 18, 21, X and Y.
 14. The method of claim 11, wherein the genetic abnormality is a structural abnormality, including but not limited to copy number changes including microdeletions and microduplications, insertions, deletions, translocations, inversions and small-size mutations including point mutations.
 15. The method of any one of claims 1 to 14, wherein the pool of TACS is fixed to a solid support.
 16. The method of claims 1 to 15, wherein the TACS are biotinylated and are bound to streptavidin-coated magnetic beads.
 17. The method of any one of claims 1 to 16, wherein amplification of the enriched library is performed in the presence of blocking sequences that inhibit amplification of wild-type sequences.
 18. The method of any one of claims 1 to 17, wherein members of the sequencing library that bind to the pool of TACS are partially complementary to the TACS.
 19. The method of any one of claims 1 to 18, wherein the statistical analysis comprises a score-based classification system. provides a read-depth for the genomic sequences of interest and read-depths for reference loci and the statistical analysis comprises applying an algorithm that tests sequentially the read-depth of the loci of from the genomic sequences of interest against the read-depth of the reference loci, the algorithm comprising steps for: (a) removal of inadequately sequenced loci; (b) GC-content bias alleviation; and (c) ploidy status determination.
 21. The method of claim 20, wherein GC-content bias is alleviated by grouping together loci of matching GC content.
 22. The method of any one of claims 1 to 18, wherein sequencing of the enriched library provides the number and size of sequenced fragments for TACS-specific coordinates and the statistical analysis comprises applying an algorithm that tests sequentially the fragment-size proportion for the genomic sequence of interest against the fragment-size proportion of the reference loci, the algorithm comprising steps for: (a) removal of fragment-size outliers; (b) fragment-size proportion calculation; and (c) ploidy status determination.
 23. A method of determining fetal risk of inheriting a genetic condition, the method comprising: (a) preparing a sequencing library from a sample comprising maternal and fetal DNA; (b) hybridizing the sequencing library to a pool of double-stranded TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of TACS families directed to different genomic sequences of interest, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest, and further wherein: (i) each member sequence within each TACS family is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end; (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within each family of TACS; (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library; (e) performing statistical analysis on the enriched library sequences to thereby determine maternal carrier status at the loci of interest associated with different genetic conditions, wherein for a sample with a positive maternal carrier status, the method further comprises: (f) obtaining a paternal DNA sample and performing steps (a)-(e) on the paternal DNA sample to determine paternal carrier status for those diseases in which there is a positive maternal carrier status; and (g) determining fetal risk of inheriting a genetic condition based on maternal carrier status and, when (f) is performed, paternal carrier status.
 24. The method of claim 23, wherein each TACS family comprises at least 3 member sequences.
 25. The method of claim 23, wherein each TACS family comprises at least 5 member sequences.
 26. The method of claim 23, wherein the pool of TACS comprises at least 5 different TACS families.
 27. The method of claim 23, wherein the pool of TACS comprises at least 50 different TACS families.
 28. The method of any one of claims 23 to 27, wherein the start and/or stop positions for the member sequences within a TACS family, with respect to a reference coordinate system for the genomic sequence of interest, are staggered by at least 3 base pairs.
 29. The method of any of any one of claims 23 to 27, wherein the start and/or stop positions for the member sequences within a TACS family, with respect to a reference coordinate system for the genomic sequence of interest, are staggered by at least 10 base pairs.
 30. The method of any one of claims 23 to 29, wherein the pool of TACS further comprises sequences that bind to chromosomes of interest for detecting fetal genetic abnormalities and further comprises performing statistical analysis on the enriched library sequences to thereby determine fetal risk of a genetic abnormality at the chromosome of interest.
 31. The method of claim 30, wherein the genetic abnormality is a chromosomal aneuploidy.
 32. The method of claim 30, wherein the genetic abnormality is a structural abnormality, including but not limited to copy number changes including microdeletions and including point mutations and mutational signatures.
 33. The method of any one of claims 23 to 32, wherein the GC content of the TACS is between 19% and 46%.
 34. The method of any one of claims 23 to 33, wherein each member sequence within each family of TACS is at least 160 base pairs in length.
 35. The method of any one of claims 23 to 34, wherein the sample is a maternal plasma sample.
 36. The method of any one of claims 23 to 35, wherein the pool of TACS comprise members that bind to chromosomes 1-22, X and Y of the human genome.
 37. The method of any one of claims 23 to 36, wherein the variant allele loci of interest are associated with genetic conditions selected from a group comprising Abetalipoproteinemia; Arthrogryposis Mental Retardation Seizures; Autosomal recessive polycystic kidney disease; Bardet Biedl syndrome 12; Beta thalassemia; Canavan disease; Choreacanthocytosis; Crigler Najjar syndrome, Type I; Cystic fibrosis; Factor V Leiden thrombophilia; Factor XI deficiency; Familial dysautonomia; Familial Mediterranean fever; Fanconi anemia (FANCG-related); Glycine encephalopathy (GLDC-related); Glycogen storage disease, Type 3; Glycogen storage disease, Type 7; GRACILE Syndrome; Inclusion body myopathy, Type 2; Isovaleric acidemia; Joubert syndrome, Type 2; Junctional epidermolysis bullosa, Herlitz type; Leber congenital amaurosis (LCAS-related); Leydig cell hypoplasia [Luteinizing Hormone Resistance]; Limb girdle muscular dystrophy, Type 2E; Lipoamide Dehydrogenase Deficiency [Maple syrup urine disease, Type 3]; Lipoprotein lipase deficiency; Long chain 3-hydroxyacyl-CoA dehydrogenase deficiency; Maple syrup urine disease, Type 1B; Methylmalonic acidemia (MMAA-related); Multiple sulfatase deficiency; Navajo neurohepatopathy [MPV17-related hepatocerebral mitochondrial DNA depletion syndrome]; Neuronal ceroid lipofuscinosis (MFSD8-related); Nijmegen breakage syndrome; Ornithine translocase deficiency [Hyperornithinemia-Hyperammonemia-Homocitrullinuria (HHH) Syndrome]; Peroxisome biogenesis disorders Zellweger syndrome spectrum (PEX1-related); Peroxisome biogenesis disorders Zellweger syndrome spectrum (PEX2-related); Phenylketonurea; Pontocerebellar hypoplasia, Type 2E; Pycnodysostosis; Pyruvate dehydrogenase deficiency (PDHB-related); Retinal Dystrophy (RLBP1-related) [Bothnia retinal dystrophy]; Retinitis pigmentosa (DHDDS-related); Sanfilippo syndrome, Type D [Mucopolysaccharidosis IIID]; Sickle-cell disease; Sjögren-Larsson Deficiency 1; 3 Methylcrotonyl CoA Carboxylase Deficiency 2, and combinations thereof.
 38. A kit for performing the method of claim 23, wherein the kit comprises a container comprising the pool of TACS and instructions for performing the method, wherein the pool of TACS comprises a plurality of TACS families, wherein each TACS family comprises a plurality of member sequences, wherein each member sequence binds to the same genomic sequence of interest but has different start and/or stop positions with respect to a reference coordinate system for the genomic sequence of interest, and further wherein: (i) each member sequence within each TACS family is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end; (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within each family of TACS.
 39. A method of determining the status of a genetic condition, the method comprising: (a) preparing a sequencing library from a sample of DNA; (b) hybridizing the sequencing library to a pool of TArget Capture Sequences (TACS), wherein the pool of TACS comprises a plurality of sequences that bind to genomic sequences of interest including variant allele loci of interest associated with different genetic conditions, wherein: (i) each member sequence within the pool of TACS is between 100-500 base pairs in length, each member sequence having a 5′ end and a 3′ end; (ii) each member sequence binds to the same genomic sequence of interest at least 50 base pairs away, on both the 5′ end and the 3′ end, from regions harboring Copy Number Variations (CNVs), Segmental duplications or repetitive DNA elements; and (iii) the GC content of the pool of TACS is between 19% and 80%, as determined by calculating the GC content of each member within the pool of TACS. (c) isolating members of the sequencing library that bind to the pool of TACS to obtain an enriched library; (e) performing statistical analysis on the enriched library sequences to thereby determine the carrier status at the loci of interest associated with different genetic conditions.
 40. The method of claim 19, wherein the condition is selected form the group comprising disease condition, a hereditary disease condition, a chronic disease condition. 